Human Behavior-Based Particle Swarm Optimization
Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the pop...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/194706 |
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| author | Hao Liu Gang Xu Gui-yan Ding Yu-bo Sun |
| author_facet | Hao Liu Gang Xu Gui-yan Ding Yu-bo Sun |
| author_sort | Hao Liu |
| collection | DOAJ |
| description | Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c1 and c2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost. |
| format | Article |
| id | doaj-art-e662c099c6384940bc0e5a81844014a2 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e662c099c6384940bc0e5a81844014a22025-02-03T06:07:48ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/194706194706Human Behavior-Based Particle Swarm OptimizationHao Liu0Gang Xu1Gui-yan Ding2Yu-bo Sun3School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Mathematics, Nanchang University, Nanchang 330031, ChinaSchool of Science, University of Science and Technology Liaoning, Anshan 114051, ChinaSchool of Science, University of Science and Technology Liaoning, Anshan 114051, ChinaParticle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c1 and c2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.http://dx.doi.org/10.1155/2014/194706 |
| spellingShingle | Hao Liu Gang Xu Gui-yan Ding Yu-bo Sun Human Behavior-Based Particle Swarm Optimization The Scientific World Journal |
| title | Human Behavior-Based Particle Swarm Optimization |
| title_full | Human Behavior-Based Particle Swarm Optimization |
| title_fullStr | Human Behavior-Based Particle Swarm Optimization |
| title_full_unstemmed | Human Behavior-Based Particle Swarm Optimization |
| title_short | Human Behavior-Based Particle Swarm Optimization |
| title_sort | human behavior based particle swarm optimization |
| url | http://dx.doi.org/10.1155/2014/194706 |
| work_keys_str_mv | AT haoliu humanbehaviorbasedparticleswarmoptimization AT gangxu humanbehaviorbasedparticleswarmoptimization AT guiyanding humanbehaviorbasedparticleswarmoptimization AT yubosun humanbehaviorbasedparticleswarmoptimization |