Adaptive particle swarm optimization with population diversity control and its application in tandem blade optimization

Abstract

This paper proposes a new variant of particle swarm optimization, namely adaptive particle swarm optimization with population diversity control (APSO-PDC), to improve the performance of particle swarm optimization. APSO-PDC is formulated based on adaptive selection of particle roles, population diversity control, and adaptive control of parameters. The adaptive selection of particle roles which combines the evolutionary state and dynamic particle state estimation method will sort the particles into three roles to let different particles execute different search tasks during optimization process. The adaptive control of parameters which is created based on the evolutionary state and particle roles encourages the exploitation ability and enhances the algorithm’s convergence speed. The population diversity control which combines comprehensive learning strategy of the comprehensive learning particle swarm optimizer with evolutionary state to update the individual best position strengthens exploration ability and thus increases the algorithm’s robustness toward the premature convergence issue. The performance of APSO-PDC is comprehensively evaluated by 21 unimodal and multimodal functions with or without rotation. The results indicate APSO-PDC has more preferable searching accuracy, searching reliability, and convergence speed than the other well-established particle swarm optimization variants. Finally, compared with other six particle swarm optimization variants, APSO-PDC shows satisfactory performance in optimizing tandem blade. This excellent performance proves that APSO-PDC has a better control of swarm exploration and exploitation abilities.

An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems

“…Similarly [25,26], improved PSO based on an exploring strategy which can be dependently adjusted. Furthermore, [27] Attempted to set parameters of PSO adaptively while maintaining the diversity to improve its exploration capability which is similar to research done by Song et al [28] which led to enhanced PSO by an exploration and exploitation approach to increase the algorithm's robustness against premature convergence. Also, [29] defined adaptive PSO with four evolutionary states: exploration, exploitation, convergence, and jumping out.…”

Role of an Optimal Multiagent Scheduling in Different Applications Using ML

“…The population diversity control problem is selected in the proposal by author Zha oyun Song, Bo Liu et al [3] to improve optimization. The method is based on the runtime selection for adaptive and diversified controlling parameters.…”

Self-adaptive salp swarm algorithm for optimization problems

“…What Proposed to Achieve Population Diversity [76,77] Avoiding excessive gathering in promising optimal areas [78,79] Avoiding premature convergence by use the dissipation method [80,81,82] Utilized niching techniques to accelerate the convergence [83,84,85] Adaptively calibrate swarm number [86,87] Mutation method based individual level All the mentioned methods focus on increasing the initial population diversity in order to increases the robustness of the proposed algorithm toward premature convergence [77,88,89], not trapped in local optima [90], and achieve balance among exploitation and exploration [91].…”