记忆增强型的重构粒子群算法

doi:10.3778/j.issn.1002-8331.2406-0244

摘要/Abstract

摘要： 重构粒子群算法（RPSO）是基于粒子群算法（PSO）的线性系统理论分析结果而重新构建一种群体智能算法，其保留了粒子群算法的个体最优位置和全局最优位置作为学习样本的策略。RPSO具有比较好的收敛性理论支撑，简单易用。但是，重构粒子群算法丢失了种群的记忆，即粒子的历史位置和适应度等信息。为了加强对记忆的利用并提高种群的协作能力，提出了一种记忆增强型的重构粒子群算法（MERPSO）。该算法设计了经验选择策略和区块搜索策略储存记忆，构建了两个新的学习样本，并使用新的学习样本替代原本的学习样本。此外，通过引入带偏移量的加速度系数来平衡算法的局部开发和全局探索能力。实验证明，MERPSO算法在CEC2013基准测试函数集和工程设计问题上表现出更好的性能，并且所采用的策略具有一定的有效性。

关键词: 重构粒子群算法, 记忆, 学习样本, 加速度系数, CEC2013

Abstract: The restructuring particle swarm optimization (RPSO) algorithm is a collective intelligence algorithm reconstructed based on the linear system theory analysis results of the particle swarm optimization (PSO) algorithm. It retains the individual optimal position and global optimal position of the PSO as the learning exemplars strategy. RPSO has good convergence theory support and is simple and user-friendly. However, the restructuring particle swarm algorithm loses the population’s memory, which includes information on particles’ historical positions and fitness. To enhance memory utilization and improve the population's collaborative capabilities, a memory-enhanced restructuring particle swarm optimization algorithm (MERPSO) is proposed. This algorithm designs experience decision and block search strategies to store memories, constructs two new learning exemplars, and replaces the original ones with these new exemplars. Additionally, by introducing acceleration coefficients with offsets, the algorithm’s local exploitation and global exploration capabilities are balanced. Experimental results demonstrate that the MERPSO algorithm exhibits superior performance on the CEC2013 benchmark test function set and the engineering design issues, with the strategies employed showing a certain level of effectiveness.

Key words: restructuring particle swarm optimization (RPSO) algorithm, memory, learning exemplar, acceleration coefficient, CEC2013

吴炳南, 刘建华, 力尚龙, 李牧元. 记忆增强型的重构粒子群算法[J]. 计算机工程与应用, 2025, 61(9): 116-127.

WU Bingnan, LIU Jianhua, LI Shanglong, LI Muyuan. Memory-Enhanced Restructuring Particle Swarm Optimization Algorithm[J]. Computer Engineering and Applications, 2025, 61(9): 116-127.

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