文献类型：期刊文献

英文题名：A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance

作者：Jiang, Zhengfeng[1];Zhu, Daoheng[2];Li, Xiao-Yu[2];Han, Ling-Bo[2]

机构：[1]Guangxi Minzu Normal Univ, Coll Math & Comp Sci, Chongzuo 532200, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2025

卷号：15

期号：11

外文期刊名：APPLIED SCIENCES-BASEL

收录：SCI-EXPANDED(收录号：WOS:001505727100001)、、EI(收录号：20252418594687)、Scopus(收录号：2-s2.0-105007755567)、WOS

基金：This work was supported by the Scientific Research Start-up Funds of Guangdong Ocean University under Grant 060302112201; the Scientific Research Innovation Team Project of Guangxi Minzu Normal University under Grant KYTD202406; and the Integrated Characteristic Construction Project of "Institution (Research Institute)-Discipline (Degree Program)-Team (Think Tank)-Innovation Platform-Incubation Project" at Guangxi Minzu Normal University under Grant 1024/10300130.

语种：英文

外文关键词：particle swarm optimization algorithms; composite chaotic mapping; adaptive inertia weights; cross-learning; differential evolution (DE)

外文摘要：The traditional particle swarm optimization (PSO) algorithm often exhibits defects such as of slow convergence and easily falling into a local optimum. To overcome these problems, this paper proposes an enhanced variant featuring adaptive selection. Initially, a composite chaotic mapping model integrating Logistic and Sine mappings is employed to initialize the population for diversity and exploration capability. Subsequently, the global and local search capabilities of the algorithm are balanced through the introduction of adaptive inertia weights. The population is then divided into three subpopulations-elite, ordinary, and inferior particles-based on their fitness values, with each group employing a distinct position update strategy. Finally, a particle mutation strategy is incorporated to avoid convergence to local optima. Experimental results demonstrate that our algorithm outperforms existing algorithms on the standard benchmark functions. In practical engineering applications, our algorithm also has demonstrated better performance than other meta heuristic algorithms.