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�ݺ�ߣshows by User: sandpoonia / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: sandpoonia / Sun, 19 Feb 2017 06:41:51 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: sandpoonia Soft computing /slideshow/soft-computing-72319911/72319911 softcomputing-170219064151
Introduction to Soft Computing]]>
Introduction to Soft Computing]]> Sun, 19 Feb 2017 06:41:51 GMT /slideshow/soft-computing-72319911/72319911 sandpoonia@slideshare.net(sandpoonia) Soft computing sandpoonia Introduction to Soft Computing <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/softcomputing-170219064151-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to Soft Computing

Soft computing from Dr Sandeep Kumar Poonia

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0 An improved memetic search in artificial bee colony algorithm /slideshow/18-an-improved-memetic-search-in-artificial-bee-colony-algorithm/33791066 18-140422033602-phpapp02
Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The solution search equation of ABC is notably motivated by a haphazard quantity which facilitates in exploration at the cost of exploitation of the search space. Due to the large step size in the solution search equation of ABC there are chances of skipping the factual solution are higher. For that reason, this paper introduces a new search strategy in order to balance the diversity and convergence capability of the ABC. Both employed bee phase and onlooker bee phase are improved with help of a local search strategy stimulated by memetic algorithm. This paper also proposes a new strategy for fitness calculation and probability calculation. The proposed algorithm is named as Improved Memetic Search in ABC (IMeABC). It is tested over 13 impartial benchmark functions of different complexities and two real word problems are also considered to prove proposed algorithms superiority over original ABC algorithm and its recent variants]]>
Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The solution search equation of ABC is notably motivated by a haphazard quantity which facilitates in exploration at the cost of exploitation of the search space. Due to the large step size in the solution search equation of ABC there are chances of skipping the factual solution are higher. For that reason, this paper introduces a new search strategy in order to balance the diversity and convergence capability of the ABC. Both employed bee phase and onlooker bee phase are improved with help of a local search strategy stimulated by memetic algorithm. This paper also proposes a new strategy for fitness calculation and probability calculation. The proposed algorithm is named as Improved Memetic Search in ABC (IMeABC). It is tested over 13 impartial benchmark functions of different complexities and two real word problems are also considered to prove proposed algorithms superiority over original ABC algorithm and its recent variants]]> Tue, 22 Apr 2014 03:36:02 GMT /slideshow/18-an-improved-memetic-search-in-artificial-bee-colony-algorithm/33791066 sandpoonia@slideshare.net(sandpoonia) An improved memetic search in artificial bee colony algorithm sandpoonia Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The solution search equation of ABC is notably motivated by a haphazard quantity which facilitates in exploration at the cost of exploitation of the search space. Due to the large step size in the solution search equation of ABC there are chances of skipping the factual solution are higher. For that reason, this paper introduces a new search strategy in order to balance the diversity and convergence capability of the ABC. Both employed bee phase and onlooker bee phase are improved with help of a local search strategy stimulated by memetic algorithm. This paper also proposes a new strategy for fitness calculation and probability calculation. The proposed algorithm is named as Improved Memetic Search in ABC (IMeABC). It is tested over 13 impartial benchmark functions of different complexities and two real word problems are also considered to prove proposed algorithms superiority over original ABC algorithm and its recent variants <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/18-140422033602-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The solution search equation of ABC is notably motivated by a haphazard quantity which facilitates in exploration at the cost of exploitation of the search space. Due to the large step size in the solution search equation of ABC there are chances of skipping the factual solution are higher. For that reason, this paper introduces a new search strategy in order to balance the diversity and convergence capability of the ABC. Both employed bee phase and onlooker bee phase are improved with help of a local search strategy stimulated by memetic algorithm. This paper also proposes a new strategy for fitness calculation and probability calculation. The proposed algorithm is named as Improved Memetic Search in ABC (IMeABC). It is tested over 13 impartial benchmark functions of different complexities and two real word problems are also considered to prove proposed algorithms superiority over original ABC algorithm and its recent variants

An improved memetic search in artificial bee colony algorithm from Dr Sandeep Kumar Poonia

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0 Modified position update in spider monkey optimization algorithm /slideshow/17-modified-position-update-in-spider-monkey-optimization-algorithm/33791063 17-140422033559-phpapp01
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.]]>
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.]]> Tue, 22 Apr 2014 03:35:59 GMT /slideshow/17-modified-position-update-in-spider-monkey-optimization-algorithm/33791063 sandpoonia@slideshare.net(sandpoonia) Modified position update in spider monkey optimization algorithm sandpoonia Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/17-140422033559-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.

Modified position update in spider monkey optimization algorithm from Dr Sandeep Kumar Poonia

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0 Enhanced local search in artificial bee colony algorithm /slideshow/16-enhanced-local-search-in-artificial-bee-colony-algorithm/33791061 16-140422033556-phpapp01
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.]]>
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.]]> Tue, 22 Apr 2014 03:35:55 GMT /slideshow/16-enhanced-local-search-in-artificial-bee-colony-algorithm/33791061 sandpoonia@slideshare.net(sandpoonia) Enhanced local search in artificial bee colony algorithm sandpoonia Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/16-140422033556-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.

Enhanced local search in artificial bee colony algorithm from Dr Sandeep Kumar Poonia

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0 RMABC /sandpoonia/15-rmabc 15-140422033551-phpapp01
Artificial Bee Colony (ABC) optimization algorithm is one of the recent population based probabilistic approach developed for global optimization. ABC is simple and has been showed significant improvement over other Nature Inspired Algorithms (NIAs) when tested over some standard benchmark functions and for some complex real world optimization problems. Memetic Algorithms also become one of the key methodologies to solve the very large and complex real-world optimization problems. The solution search equation of Memetic ABC is based on Golden Section Search and an arbitrary value which tries to balance exploration and exploitation of search space. But still there are some chances to skip the exact solution due to its step size. In order to balance between diversification and intensification capability of the Memetic ABC, it is randomized the step size in Memetic ABC. The proposed algorithm is named as Randomized Memetic ABC (RMABC). In RMABC, new solutions are generated nearby the best so far solution and it helps to increase the exploitation capability of Memetic ABC. The experiments on some test problems of different complexities and one well known engineering optimization application show that the proposed algorithm outperforms over Memetic ABC (MeABC) and some other variant of ABC algorithm(like Gbest guided ABC (GABC),Hooke Jeeves ABC (HJABC), Best-So-Far ABC (BSFABC) and Modified ABC (MABC) in case of almost all the problems.]]>
Artificial Bee Colony (ABC) optimization algorithm is one of the recent population based probabilistic approach developed for global optimization. ABC is simple and has been showed significant improvement over other Nature Inspired Algorithms (NIAs) when tested over some standard benchmark functions and for some complex real world optimization problems. Memetic Algorithms also become one of the key methodologies to solve the very large and complex real-world optimization problems. The solution search equation of Memetic ABC is based on Golden Section Search and an arbitrary value which tries to balance exploration and exploitation of search space. But still there are some chances to skip the exact solution due to its step size. In order to balance between diversification and intensification capability of the Memetic ABC, it is randomized the step size in Memetic ABC. The proposed algorithm is named as Randomized Memetic ABC (RMABC). In RMABC, new solutions are generated nearby the best so far solution and it helps to increase the exploitation capability of Memetic ABC. The experiments on some test problems of different complexities and one well known engineering optimization application show that the proposed algorithm outperforms over Memetic ABC (MeABC) and some other variant of ABC algorithm(like Gbest guided ABC (GABC),Hooke Jeeves ABC (HJABC), Best-So-Far ABC (BSFABC) and Modified ABC (MABC) in case of almost all the problems.]]> Tue, 22 Apr 2014 03:35:51 GMT /sandpoonia/15-rmabc sandpoonia@slideshare.net(sandpoonia) RMABC sandpoonia Artificial Bee Colony (ABC) optimization algorithm is one of the recent population based probabilistic approach developed for global optimization. ABC is simple and has been showed significant improvement over other Nature Inspired Algorithms (NIAs) when tested over some standard benchmark functions and for some complex real world optimization problems. Memetic Algorithms also become one of the key methodologies to solve the very large and complex real-world optimization problems. The solution search equation of Memetic ABC is based on Golden Section Search and an arbitrary value which tries to balance exploration and exploitation of search space. But still there are some chances to skip the exact solution due to its step size. In order to balance between diversification and intensification capability of the Memetic ABC, it is randomized the step size in Memetic ABC. The proposed algorithm is named as Randomized Memetic ABC (RMABC). In RMABC, new solutions are generated nearby the best so far solution and it helps to increase the exploitation capability of Memetic ABC. The experiments on some test problems of different complexities and one well known engineering optimization application show that the proposed algorithm outperforms over Memetic ABC (MeABC) and some other variant of ABC algorithm(like Gbest guided ABC (GABC),Hooke Jeeves ABC (HJABC), Best-So-Far ABC (BSFABC) and Modified ABC (MABC) in case of almost all the problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/15-140422033551-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Bee Colony (ABC) optimization algorithm is one of the recent population based probabilistic approach developed for global optimization. ABC is simple and has been showed significant improvement over other Nature Inspired Algorithms (NIAs) when tested over some standard benchmark functions and for some complex real world optimization problems. Memetic Algorithms also become one of the key methodologies to solve the very large and complex real-world optimization problems. The solution search equation of Memetic ABC is based on Golden Section Search and an arbitrary value which tries to balance exploration and exploitation of search space. But still there are some chances to skip the exact solution due to its step size. In order to balance between diversification and intensification capability of the Memetic ABC, it is randomized the step size in Memetic ABC. The proposed algorithm is named as Randomized Memetic ABC (RMABC). In RMABC, new solutions are generated nearby the best so far solution and it helps to increase the exploitation capability of Memetic ABC. The experiments on some test problems of different complexities and one well known engineering optimization application show that the proposed algorithm outperforms over Memetic ABC (MeABC) and some other variant of ABC algorithm(like Gbest guided ABC (GABC),Hooke Jeeves ABC (HJABC), Best-So-Far ABC (BSFABC) and Modified ABC (MABC) in case of almost all the problems.

RMABC from Dr Sandeep Kumar Poonia

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0 Memetic search in differential evolution algorithm /slideshow/14-memetic-search-in-differential-evolution-algorithm/33791056 14-140422033547-phpapp01
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.]]>
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.]]> Tue, 22 Apr 2014 03:35:47 GMT /slideshow/14-memetic-search-in-differential-evolution-algorithm/33791056 sandpoonia@slideshare.net(sandpoonia) Memetic search in differential evolution algorithm sandpoonia Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/14-140422033547-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.

Memetic search in differential evolution algorithm from Dr Sandeep Kumar Poonia

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0 Improved onlooker bee phase in artificial bee colony algorithm /slideshow/13-improved-onlooker-bee-phase-in-artificial-bee-colony-algorithm/33791051 13-140422033543-phpapp01
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.]]>
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.]]> Tue, 22 Apr 2014 03:35:43 GMT /slideshow/13-improved-onlooker-bee-phase-in-artificial-bee-colony-algorithm/33791051 sandpoonia@slideshare.net(sandpoonia) Improved onlooker bee phase in artificial bee colony algorithm sandpoonia Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/13-140422033543-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.

Improved onlooker bee phase in artificial bee colony algorithm from Dr Sandeep Kumar Poonia

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0 Comparative study of_hybrids_of_artificial_bee_colony_algorithm /slideshow/12-comparative-study-ofhybridsofartificialbeecolonyalgorithm/33791048 12-140422033539-phpapp01
Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS]]>
Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS]]> Tue, 22 Apr 2014 03:35:38 GMT /slideshow/12-comparative-study-ofhybridsofartificialbeecolonyalgorithm/33791048 sandpoonia@slideshare.net(sandpoonia) Comparative study of_hybrids_of_artificial_bee_colony_algorithm sandpoonia Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/12-140422033539-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS

Comparative study of_hybrids_of_artificial_bee_colony_algorithm from Dr Sandeep Kumar Poonia

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0 A novel hybrid crossover based abc algorithm /slideshow/9-a-novel-hybrid-crossover-based-abc-algorithm/33791041 9-140422033527-phpapp02
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.]]>
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.]]> Tue, 22 Apr 2014 03:35:27 GMT /slideshow/9-a-novel-hybrid-crossover-based-abc-algorithm/33791041 sandpoonia@slideshare.net(sandpoonia) A novel hybrid crossover based abc algorithm sandpoonia Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/9-140422033527-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.

A novel hybrid crossover based abc algorithm from Dr Sandeep Kumar Poonia

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0 Multiplication of two 3 d sparse matrices using 1d arrays and linked lists /slideshow/7-multiplication-of-two-3-d-sparse-matrices-using-1d-arrays-and-linked-lists/33791033 7-140422033519-phpapp02
A basic algorithm of 3D sparse matrix multiplication (BASMM) is presented using one dimensional (1D) arrays which is used further for multiplying two 3D sparse matrices using Linked Lists. In this algorithm, a general concept is derived in which we enter non- zeros elements in 1st and 2nd sparse matrices (3D) but store that values in 1D arrays and linked lists so that zeros could be removed or ignored to store in memory. The positions of that non-zero value are also stored in memory like row and column position. In this way space complexity is decreased. There are two ways to store the sparse matrix in memory. First is row major order and another is column major order. But, in this algorithm, row major order is used. Now multiplying those two matrices with the help of BASMM algorithm, time complexity also decreased. For the implementation of this, simple c programming and concepts of data structures are used which are very easy to understand for everyone.]]>
A basic algorithm of 3D sparse matrix multiplication (BASMM) is presented using one dimensional (1D) arrays which is used further for multiplying two 3D sparse matrices using Linked Lists. In this algorithm, a general concept is derived in which we enter non- zeros elements in 1st and 2nd sparse matrices (3D) but store that values in 1D arrays and linked lists so that zeros could be removed or ignored to store in memory. The positions of that non-zero value are also stored in memory like row and column position. In this way space complexity is decreased. There are two ways to store the sparse matrix in memory. First is row major order and another is column major order. But, in this algorithm, row major order is used. Now multiplying those two matrices with the help of BASMM algorithm, time complexity also decreased. For the implementation of this, simple c programming and concepts of data structures are used which are very easy to understand for everyone.]]> Tue, 22 Apr 2014 03:35:19 GMT /slideshow/7-multiplication-of-two-3-d-sparse-matrices-using-1d-arrays-and-linked-lists/33791033 sandpoonia@slideshare.net(sandpoonia) Multiplication of two 3 d sparse matrices using 1d arrays and linked lists sandpoonia A basic algorithm of 3D sparse matrix multiplication (BASMM) is presented using one dimensional (1D) arrays which is used further for multiplying two 3D sparse matrices using Linked Lists. In this algorithm, a general concept is derived in which we enter non- zeros elements in 1st and 2nd sparse matrices (3D) but store that values in 1D arrays and linked lists so that zeros could be removed or ignored to store in memory. The positions of that non-zero value are also stored in memory like row and column position. In this way space complexity is decreased. There are two ways to store the sparse matrix in memory. First is row major order and another is column major order. But, in this algorithm, row major order is used. Now multiplying those two matrices with the help of BASMM algorithm, time complexity also decreased. For the implementation of this, simple c programming and concepts of data structures are used which are very easy to understand for everyone. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/7-140422033519-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A basic algorithm of 3D sparse matrix multiplication (BASMM) is presented using one dimensional (1D) arrays which is used further for multiplying two 3D sparse matrices using Linked Lists. In this algorithm, a general concept is derived in which we enter non- zeros elements in 1st and 2nd sparse matrices (3D) but store that values in 1D arrays and linked lists so that zeros could be removed or ignored to store in memory. The positions of that non-zero value are also stored in memory like row and column position. In this way space complexity is decreased. There are two ways to store the sparse matrix in memory. First is row major order and another is column major order. But, in this algorithm, row major order is used. Now multiplying those two matrices with the help of BASMM algorithm, time complexity also decreased. For the implementation of this, simple c programming and concepts of data structures are used which are very easy to understand for everyone.

Multiplication of two 3 d sparse matrices using 1d arrays and linked lists from Dr Sandeep Kumar Poonia

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0 Sunzip user tool for data reduction using huffman algorithm /slideshow/6-sunzip-user-tool-for-data-reduction-using-huffman-algorithm/33791031 6-140422033515-phpapp01
Smart Huffman Compression is a software appliance designed to compress a file in a better way. By functioning as an JSP, it provides high level abstraction of java Servlet. For example, Smart Huffman Compression encodes the digital information using fewer bits, reduces the size of file without loss of data in a single, easy-to-manage software appliance form factor. It also provides us the decompression facility also. Smart Huffman Compression provides our organization with effective solutions to reduce the file size or lossless compression of data. It also expedites security of data using the encoding functionality. It is necessary to analyze the relationship between different methods and put them into a framework to better understand and better exploit the possibilities that compression provides us image compression, data compression, audio compression, video compression etc.]]>
Smart Huffman Compression is a software appliance designed to compress a file in a better way. By functioning as an JSP, it provides high level abstraction of java Servlet. For example, Smart Huffman Compression encodes the digital information using fewer bits, reduces the size of file without loss of data in a single, easy-to-manage software appliance form factor. It also provides us the decompression facility also. Smart Huffman Compression provides our organization with effective solutions to reduce the file size or lossless compression of data. It also expedites security of data using the encoding functionality. It is necessary to analyze the relationship between different methods and put them into a framework to better understand and better exploit the possibilities that compression provides us image compression, data compression, audio compression, video compression etc.]]> Tue, 22 Apr 2014 03:35:15 GMT /slideshow/6-sunzip-user-tool-for-data-reduction-using-huffman-algorithm/33791031 sandpoonia@slideshare.net(sandpoonia) Sunzip user tool for data reduction using huffman algorithm sandpoonia Smart Huffman Compression is a software appliance designed to compress a file in a better way. By functioning as an JSP, it provides high level abstraction of java Servlet. For example, Smart Huffman Compression encodes the digital information using fewer bits, reduces the size of file without loss of data in a single, easy-to-manage software appliance form factor. It also provides us the decompression facility also. Smart Huffman Compression provides our organization with effective solutions to reduce the file size or lossless compression of data. It also expedites security of data using the encoding functionality. It is necessary to analyze the relationship between different methods and put them into a framework to better understand and better exploit the possibilities that compression provides us image compression, data compression, audio compression, video compression etc. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/6-140422033515-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Smart Huffman Compression is a software appliance designed to compress a file in a better way. By functioning as an JSP, it provides high level abstraction of java Servlet. For example, Smart Huffman Compression encodes the digital information using fewer bits, reduces the size of file without loss of data in a single, easy-to-manage software appliance form factor. It also provides us the decompression facility also. Smart Huffman Compression provides our organization with effective solutions to reduce the file size or lossless compression of data. It also expedites security of data using the encoding functionality. It is necessary to analyze the relationship between different methods and put them into a framework to better understand and better exploit the possibilities that compression provides us image compression, data compression, audio compression, video compression etc.

Sunzip user tool for data reduction using huffman algorithm from Dr Sandeep Kumar Poonia

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0 New Local Search Strategy in Artificial Bee Colony Algorithm /slideshow/21-nlssabc/33791030 21-140422033513-phpapp02
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based on intelligent food foraging behaviour of honey bee swarm. This paper introduces a local search strategy that enhances exploration competence of ABC and avoids the problem of stagnation. The proposed strategy introduces two new local search phases in original ABC. One just after onlooker bee phase and one after scout bee phase. The newly introduced phases are inspired by modified Golden Section Search (GSS) strategy. The proposed strategy named as new local search strategy in ABC (NLSSABC). The proposed NLSSABC algorithm applied over thirteen standard benchmark functions in order to prove its efficiency.]]>
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based on intelligent food foraging behaviour of honey bee swarm. This paper introduces a local search strategy that enhances exploration competence of ABC and avoids the problem of stagnation. The proposed strategy introduces two new local search phases in original ABC. One just after onlooker bee phase and one after scout bee phase. The newly introduced phases are inspired by modified Golden Section Search (GSS) strategy. The proposed strategy named as new local search strategy in ABC (NLSSABC). The proposed NLSSABC algorithm applied over thirteen standard benchmark functions in order to prove its efficiency.]]> Tue, 22 Apr 2014 03:35:13 GMT /slideshow/21-nlssabc/33791030 sandpoonia@slideshare.net(sandpoonia) New Local Search Strategy in Artificial Bee Colony Algorithm sandpoonia Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based on intelligent food foraging behaviour of honey bee swarm. This paper introduces a local search strategy that enhances exploration competence of ABC and avoids the problem of stagnation. The proposed strategy introduces two new local search phases in original ABC. One just after onlooker bee phase and one after scout bee phase. The newly introduced phases are inspired by modified Golden Section Search (GSS) strategy. The proposed strategy named as new local search strategy in ABC (NLSSABC). The proposed NLSSABC algorithm applied over thirteen standard benchmark functions in order to prove its efficiency. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/21-140422033513-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based on intelligent food foraging behaviour of honey bee swarm. This paper introduces a local search strategy that enhances exploration competence of ABC and avoids the problem of stagnation. The proposed strategy introduces two new local search phases in original ABC. One just after onlooker bee phase and one after scout bee phase. The newly introduced phases are inspired by modified Golden Section Search (GSS) strategy. The proposed strategy named as new local search strategy in ABC (NLSSABC). The proposed NLSSABC algorithm applied over thirteen standard benchmark functions in order to prove its efficiency.

New Local Search Strategy in Artificial Bee Colony Algorithm from Dr Sandeep Kumar Poonia

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0 A new approach of program slicing /sandpoonia/5-a-new-approach-of-program-slicing 5-140422033511-phpapp02
Program slicing technique is used for decomposition of a program by analyzing that particular program data and control flow. The main application of program slicing includes various software engineering activities such as program debugging, understanding, program maintenance, and testing and complexity measurement. When a slicing technique gathers information about the data and control flow of the program taking an actual and specific execution (or set of executions) of it, then it is said to be dynamic slicing, otherwise it is said to be static slicing. Generally, dynamic slices are smaller than static because the statements of the program that affect by the slicing criterion for a particular execution are contained by dynamic slicing. This paper reports a new approach of program slicing that is a mixed approach of static and dynamic slice (S-D slicing) using Object Oriented Concepts in C++ Language that will reduce the complexity of the program and simplify the program for various software engineering applications like program debubbing.]]>
Program slicing technique is used for decomposition of a program by analyzing that particular program data and control flow. The main application of program slicing includes various software engineering activities such as program debugging, understanding, program maintenance, and testing and complexity measurement. When a slicing technique gathers information about the data and control flow of the program taking an actual and specific execution (or set of executions) of it, then it is said to be dynamic slicing, otherwise it is said to be static slicing. Generally, dynamic slices are smaller than static because the statements of the program that affect by the slicing criterion for a particular execution are contained by dynamic slicing. This paper reports a new approach of program slicing that is a mixed approach of static and dynamic slice (S-D slicing) using Object Oriented Concepts in C++ Language that will reduce the complexity of the program and simplify the program for various software engineering applications like program debubbing.]]> Tue, 22 Apr 2014 03:35:11 GMT /sandpoonia/5-a-new-approach-of-program-slicing sandpoonia@slideshare.net(sandpoonia) A new approach of program slicing sandpoonia Program slicing technique is used for decomposition of a program by analyzing that particular program data and control flow. The main application of program slicing includes various software engineering activities such as program debugging, understanding, program maintenance, and testing and complexity measurement. When a slicing technique gathers information about the data and control flow of the program taking an actual and specific execution (or set of executions) of it, then it is said to be dynamic slicing, otherwise it is said to be static slicing. Generally, dynamic slices are smaller than static because the statements of the program that affect by the slicing criterion for a particular execution are contained by dynamic slicing. This paper reports a new approach of program slicing that is a mixed approach of static and dynamic slice (S-D slicing) using Object Oriented Concepts in C++ Language that will reduce the complexity of the program and simplify the program for various software engineering applications like program debubbing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5-140422033511-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Program slicing technique is used for decomposition of a program by analyzing that particular program data and control flow. The main application of program slicing includes various software engineering activities such as program debugging, understanding, program maintenance, and testing and complexity measurement. When a slicing technique gathers information about the data and control flow of the program taking an actual and specific execution (or set of executions) of it, then it is said to be dynamic slicing, otherwise it is said to be static slicing. Generally, dynamic slices are smaller than static because the statements of the program that affect by the slicing criterion for a particular execution are contained by dynamic slicing. This paper reports a new approach of program slicing that is a mixed approach of static and dynamic slice (S-D slicing) using Object Oriented Concepts in C++ Language that will reduce the complexity of the program and simplify the program for various software engineering applications like program debubbing.

A new approach of program slicing from Dr Sandeep Kumar Poonia

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0 Performance evaluation of different routing protocols in wsn using different network parameters for small terrain area /slideshow/3-performance-evaluation-of-different-routing-protocols-in-wsn-using-different-network-parameters-for-small-terrain-area/33791027 3-140422033508-phpapp01
]]>
]]> Tue, 22 Apr 2014 03:35:08 GMT /slideshow/3-performance-evaluation-of-different-routing-protocols-in-wsn-using-different-network-parameters-for-small-terrain-area/33791027 sandpoonia@slideshare.net(sandpoonia) Performance evaluation of different routing protocols in wsn using different network parameters for small terrain area sandpoonia <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3-140422033508-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Performance evaluation of different routing protocols in wsn using different network parameters for small terrain area from Dr Sandeep Kumar Poonia

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0 Enhanced abc algo for tsp /slideshow/2-enhanced-abc-algo-for-tsp/33791024 2-140422033502-phpapp01
Articial bee Colony algorithm (ABC) is a population based heuristic search technique used for optimization problems. ABC is a very eective optimization technique for continuous opti- mization problem. Crossover operators have a better exploration property so crossover operators are added to the ABC. This pa- per presents ABC with dierent types of real coded crossover op- erator and its application to Travelling Salesman Problem (TSP). Each crossover operator is applied to two randomly selected par- ents from current swarm. Two o-springs generated from crossover and worst parent is replaced by best ospring, other parent remains same. ABC with real coded crossover operator applied to travelling salesman problem. The experimental result shows that our proposed algorithm performs better than the ABC without crossover in terms of eciency and accuracy.]]>
Articial bee Colony algorithm (ABC) is a population based heuristic search technique used for optimization problems. ABC is a very eective optimization technique for continuous opti- mization problem. Crossover operators have a better exploration property so crossover operators are added to the ABC. This pa- per presents ABC with dierent types of real coded crossover op- erator and its application to Travelling Salesman Problem (TSP). Each crossover operator is applied to two randomly selected par- ents from current swarm. Two o-springs generated from crossover and worst parent is replaced by best ospring, other parent remains same. ABC with real coded crossover operator applied to travelling salesman problem. The experimental result shows that our proposed algorithm performs better than the ABC without crossover in terms of eciency and accuracy.]]> Tue, 22 Apr 2014 03:35:02 GMT /slideshow/2-enhanced-abc-algo-for-tsp/33791024 sandpoonia@slideshare.net(sandpoonia) Enhanced abc algo for tsp sandpoonia Arti�cial bee Colony algorithm (ABC) is a population based heuristic search technique used for optimization problems. ABC is a very e�ective optimization technique for continuous opti- mization problem. Crossover operators have a better exploration property so crossover operators are added to the ABC. This pa- per presents ABC with di�erent types of real coded crossover op- erator and its application to Travelling Salesman Problem (TSP). Each crossover operator is applied to two randomly selected par- ents from current swarm. Two o�-springs generated from crossover and worst parent is replaced by best o�spring, other parent remains same. ABC with real coded crossover operator applied to travelling salesman problem. The experimental result shows that our proposed algorithm performs better than the ABC without crossover in terms of e�ciency and accuracy. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-140422033502-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Arti�cial bee Colony algorithm (ABC) is a population based heuristic search technique used for optimization problems. ABC is a very e�ective optimization technique for continuous opti- mization problem. Crossover operators have a better exploration property so crossover operators are added to the ABC. This pa- per presents ABC with di�erent types of real coded crossover op- erator and its application to Travelling Salesman Problem (TSP). Each crossover operator is applied to two randomly selected par- ents from current swarm. Two o�-springs generated from crossover and worst parent is replaced by best o�spring, other parent remains same. ABC with real coded crossover operator applied to travelling salesman problem. The experimental result shows that our proposed algorithm performs better than the ABC without crossover in terms of e�ciency and accuracy.

Enhanced abc algo for tsp from Dr Sandeep Kumar Poonia

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0 Database aggregation using metadata /slideshow/1-database-aggregation-using-metadata/33791022 1-140422033458-phpapp02
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]]> Tue, 22 Apr 2014 03:34:58 GMT /slideshow/1-database-aggregation-using-metadata/33791022 sandpoonia@slideshare.net(sandpoonia) Database aggregation using metadata sandpoonia <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1-140422033458-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Database aggregation using metadata from Dr Sandeep Kumar Poonia

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0 Performance evaluation of diff routing protocols in wsn using difft network parameters for large terrain area /sandpoonia/4-performance-evaluation-of-diff-routing-protocols-in-wsn-using-difft-network-parameters-for-large-terrain-area 4-140422033456-phpapp01
In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network.]]>
In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network.]]> Tue, 22 Apr 2014 03:34:55 GMT /sandpoonia/4-performance-evaluation-of-diff-routing-protocols-in-wsn-using-difft-network-parameters-for-large-terrain-area sandpoonia@slideshare.net(sandpoonia) Performance evaluation of diff routing protocols in wsn using difft network parameters for large terrain area sandpoonia In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/4-140422033456-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network.

Performance evaluation of diff routing protocols in wsn using difft network parameters for large terrain area from Dr Sandeep Kumar Poonia

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0 Lecture28 tsp /slideshow/lecture28-tsp/33728847 lecture28-tsp-140420072441-phpapp01
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Lecture28 tsp from Dr Sandeep Kumar Poonia

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0 Lecture27 linear programming /sandpoonia/lecture27-linear-programming lecture27-linearprogramming-140420072418-phpapp01
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Lecture27 linear programming from Dr Sandeep Kumar Poonia

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0 Lecture26 /slideshow/lecture26-33728794/33728794 lecture26-140420072056-phpapp01
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Lecture26 from Dr Sandeep Kumar Poonia

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0 https://cdn.slidesharecdn.com/profile-photo-sandpoonia-48x48.jpg?cb=1672721011 Dr. Sandeep Kumar is currently an Associate Professor at CHRIST (Deemed to be University), Bengaluru, India and Post Doctoral research fellow at Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia. He was an assistant professor at ACEIT, Jaipur (2008–2011), Jagannath University, Jaipur (2011–2017) and Amity University Rajasthan, Jaipur, India (2017-2020). He edited special issues for many journals, including IJGUC, IJIIDS, IJARGE, IJESD, JIM, JDMSC, JSMS, and JIOS. www.sandeeppoonia.com/ https://cdn.slidesharecdn.com/ss_thumbnails/softcomputing-170219064151-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/soft-computing-72319911/72319911 Soft computing https://cdn.slidesharecdn.com/ss_thumbnails/18-140422033602-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/18-an-improved-memetic-search-in-artificial-bee-colony-algorithm/33791066 An improved memetic se... https://cdn.slidesharecdn.com/ss_thumbnails/17-140422033559-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/17-modified-position-update-in-spider-monkey-optimization-algorithm/33791063 Modified position upda...