标准粒子群优化算法收敛性能分析与参数选择
onvergence Analysis and Parameters Selection of Standard Particle Swarm Optimization Algorithm
基于离散时间线性动态系统理论,推导了确定性标准粒子群优化(PSO)算法收敛,临界稳定与发散的充分必要条件,并用若干个粒子运动轨迹的仿真结果对理论结果进行了验证。根据理论分析结果,给出了参数选择的指导方法,讨论了随机参数对PSO算法性能的影响,分析了不同收敛阶段PSO算法探索与开发能力的平衡问题。同时本文指出,目前较普遍认为的:PSO算法的惯性权越小则局部搜索能力越强这一观点严格说来并不够准确,在实际中应小心使用。
Based on the discrete time linear dynamic system theory, the formal sufficient and necessary condition for the deterministic standard PSO algorithm to converge to equilibrium point, diverge to infinity or oscillate within a range is derived. Several simulation results of particle trajectories are given to illustrate and verify the theory analysis. Based on the theory analysis, a general guideline for parameters selection is provided; the effect of randomness is discussed and the exploration and exploitation tradeoff of PSO algorithm at different stages is analyzed. It is also pointed out that, strictly speaking, the currently popular view that small inertia weight will facilitate a local search is not accurate enough. It should be used carefully in practice.
林川、冯全源
自动化基础理论计算技术、计算机技术
粒子群,随机优化,收敛性,参数选择
Particle swarm Stochastic Optimization Convergence analysis Parameter selection
林川,冯全源.标准粒子群优化算法收敛性能分析与参数选择[EB/OL].(2007-11-23)[2025-08-23].http://www.paper.edu.cn/releasepaper/content/200711-481.点此复制
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