计算机工程与应用2024，Vol.60Issue(8)：78-89,12.DOI:10.3778/j.issn.1002-8331.2307-0368
混合驱动的粒子群算法
Hybrid Driven Particle Swarm Algorithm
摘要
Abstract
The particle swarm optimization(PSO)algorithm is a widely applied optimization algorithm in fields such as robot motion planning and signal processing.However,this algorithm is prone to getting stuck in local optima,resulting in premature convergence problem.One reason for this premature convergence problem is that the particle swarm relies solely on fitness values to select learning examples.To overcome this problem,a particle swarm optimization algorithm called FINPSO(particle swarm optimization algorithm based on a hybrid approach driven by fitness values,improvement rate,and novelty)is proposed.The algorithm introduces new metrics and utilizes a genetic algorithm to balance the explo-ration and exploitation of the population,reducing the likelihood of premature convergence in the particle swarm.Firstly,fitness values,improvement rate,and novelty are used as evaluation metrics for the particles.Each metric is independently employed to select learning examples,which are then stored in separate archives.During each velocity update,particles need to determine the weights of each metric and learn by selecting an example from each archive.Secondly,the algo-rithm incorporates a genetic algorithm for information exchange among particles.The genetic algorithm introduces cross-swapping and mutation,bringing more randomness to the population and enhancing its global search capability.Finally,eight variants of the PSO algorithm are used as comparative algorithms,and two CEC test suites are employed as bench-mark functions in the experiments.The experimental results demonstrate that the FINPSO algorithm outperforms the existing PSO algorithm variants,reaching a state-of-the-art level.关键词
粒子群优化/遗传算法/混合驱动/全局优化算法/进化算法Key words
particle swarm optimization/genetic algorithm/hybrid drive/global optimization algorithm/evolutionary algorithm分类
信息技术与安全科学引用本文复制引用
陈峰,丁泉,吴乐,刘爱萍,陈勋,张云飞..混合驱动的粒子群算法[J].计算机工程与应用,2024,60(8):78-89,12.基金项目
国家重点研发计划(2021YFF0501600). （2021YFF0501600）
