title: Human behavior-based particle swarm optimization.
abstract: 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 c 1 and c 2 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.
authors.affiliation: School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China ; School of Science, University of Science and Technology Liaoning, Anshan 114051, China.
authors.forename: Hao
authors.identifier: 0000-0003-3607-7176
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Mathematics, Nanchang University, Nanchang 330031, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Xu
authors.affiliation: School of Science, University of Science and Technology Liaoning, Anshan 114051, China.
authors.forename: Gui-Yan
authors.initials: GY
authors.lastname: Ding
authors.affiliation: School of Science, University of Science and Technology Liaoning, Anshan 114051, China.
authors.forename: Yu-Bo
authors.initials: YB
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001519:Behavior; D019985:Benchmarking; D017598:Efficiency, Organizational; D006801:Humans; D008962:Models, Theoretical; D013223:Statistics as Topic / Q000379:methods