Improved global-best-guided particle swarm optimization with learning operation for global optimization problems

• Published in: Applied soft computing Vol. 52; pp. 987 - 1008
• Main Authors: Ouyang, Hai-bin, Gao, Li-qun, Li, Steven, Kong, Xiang-yong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2017
• Subjects: Accuracy; Convergence speed; Global exploration capability; Particle swarm optimization; Accuracy; Global exploration capability; Particle swarm optimization; Convergence speed
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.09.030

[Display omitted] •A new population partitioning strategy is employed to PSO algorithm.•In the current population, global neighborhood exploration strategy is presented to enhance the global exploration capability.•A local learning mechanism is used to improve local exploitation ability in historical best population.•Stochastic learning and opposition based learning operations are employed to accelerate convergence speed and improve optimization accuracy in global best population.•IGPSO performs better for engineering design optimization problems. In this paper, an improved global-best-guided particle swarm optimization with learning operation (IGPSO) is proposed for solving global optimization problems. The particle population is divided into current population, historical best population and global best population, and each population is assigned a corresponding searching strategy. For the current population, the global neighborhood exploration strategy is employed to enhance the global exploration capability. A local learning mechanism is used to improve local exploitation ability in the historical best population. Furthermore, stochastic learning and opposition based learning operations are employed to the global best population for accelerating convergence speed and improving optimization accuracy. The effects of the relevant parameters on the performance of IGPSO are assessed. Numerical experiments on some well-known benchmark test functions reveal that IGPSO algorithm outperforms other state-of-the-art intelligent algorithms in terms of accuracy, convergence speed, and nonparametric statistical significance. Moreover, IGPSO performs better for engineering design optimization problems.