基于多种策略的改进粒子群优化算法

doi:10.12068/j.issn.1005-3026.2023.08.004

摘要/Abstract

摘要： 针对粒子群优化算法收敛速度慢、难以跳出局部最优解的问题，使用多种策略对标准粒子群优化算法进行改进，提出了混合动态粒子群优化(hybrid dynamic particle swarm optimization，HDPSO)算法.该算法按比例将粒子分为优势粒子和劣势粒子，使用不同公式分别计算每个粒子的惯性权重；为每个粒子单独设置变异系数，记录粒子相邻两次适应度变化较小的持续次数，若大于阈值则开始累加变异系数，变异系数达到上限值后重新设为初始值；在位置更新公式中引入加速系数提高算法的收敛速度.采用标准测试函数对HDPSO算法和其他粒子群优化算法进行了测试.结果表明HDPSO算法在收敛速度、寻优精度和稳定性方面具有明显的优势，进而证明所提方法的有效性.

关键词: 粒子群优化算法；自适应惯性权重；变异；加速系数；局部最优解

Abstract: Aiming the problem of slow convergence speed and difficulty in jumping out of the local optimal solution of particle swarm optimization (PSO) algorithm， the standard PSO algorithm is improved with various strategies， and the hybrid dynamic PSO (HDPSO) algorithm is proposed. The algorithm divides particles into dominant particles and inferior particles according to a proportion， and uses different formulas to calculate the inertia weights of each particle respectively. Each particle is assigned a variation coefficient， and the duration of small fitness changes between adjacent particles is recorded. If this duration exceeds a threshold value， the variation coefficient is accumulated and reset to the initial value after reaching the upper limit. The acceleration coefficient is introduced into the position update formula to improve the convergence speed of the algorithm. The standard test functions are used to test the HDPSO algorithm and other PSO algorithms. The results show that HDPSO algorithm has obvious advantages in terms of convergence speed， optimization accuracy and stability， which further proves the effectiveness of the proposed method.

Key words: particle swarm optimization (PSO) algorithm; adaptive inertia weight; mutation; acceleration coefficient; local optimal solution

中图分类号:

TP311

康岩松，臧顺来. 基于多种策略的改进粒子群优化算法[J]. 东北大学学报（自然科学版）, 2023, 44(8): 1089-1097.

KANG Yan-song， ZANG Shun-lai. Improved Particle Swarm Optimization Algorithm Based on Multiple Strategies[J]. Journal of Northeastern University(Natural Science), 2023, 44(8): 1089-1097.

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