Fast comprehensive learning particle swarm optimization

Abstract

[Objective] The particle swarm optimization algorithm is widely used in research fields such as inverse problem solving, function optimization, data mining, and machine learning, but it still faces the problem of premature convergence when solving complex multimodal problems. In order to improve the speed and accuracy of traditional particle swarm optimization in handling complex multimodal problems, this paper proposes the Fast Comprehensive Learning Particle Swarm Optimization algorithm( FCLPSO). [Methods] The FCLPSO algorithm introduces three attributes: learning probability curve, presonal probability, and group influence probability, to characterize the different learning abilities of each individual particle. At the same time, strategies such as reinforcement learning and particle rebirth are added to improve the convergence speed of the algorithm and monitor and jump out of the " pseudo convergence" state. 14 standard benchmark test functions and 6 commonly used particle swarm optimization variant algorithms were selected for performance analysis of the FCLPSO algorithm. [Results] The result showed that in terms of convergence, the average ranking of the FCLPSO algorithm was 1. 86, with 7 times ranking first, 2 times ranking second, and 0 times ranking last and the overall ranking was first; In terms of robustness, the FCLPSO algorithm ranks first with an average success rate of 94. 3%, and the lowest success rate among the 14 test functions is 73. 3%; The minimum number of fitness evaluations required to reach the threshold is 40817, which is half the number of evaluations compared to other algorithms. [Conclusion] The result indicate that the FCLPSO algorithm ranks first in terms of convergence accuracy, convergence speed, and robustness, and has more advantages in solving complex multimodal problems. It can provide an important means for solving complex optimization problems in engineering applications.

Keywords

particle swarm optimization / reinforcement learning / particle properties / particle reinitialize / premature convergence / influencing factors / artificial intelligence / global search