混沌粒子群混合优化算法
首发时间:2007-02-12
摘要:粒子群优化算法(PSO)具有收敛速度快但易陷入局部最优点的特点,因此本文将在结合混沌运动的遍历性、伪随机性和对初值的敏感性等特点的基础上,对粒子群优化算法进行了改进,提出了一种基于混沌思想的粒子群优化算法(CPSO),该算法保持了群体多样性,增强了PSO算法的全局寻优能力,提高了算法的计算精度,改善了收敛性和鲁棒性,很大程度上避免了算法停滞现象的发生,是一种有效的优化搜索算法。
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Hybrid Particle Swam with Chaos Optimization Algorithm
Abstract:Particle swam optimization algorithm (PSO) had quickly convergence but easily trapped into the local optimum. So this article will from the base of the chaos of enumeration, random and sensitivity for initial values, give a new better algorithm which based on the chaos of the particle swam optimization (CPSO), was presented through the improvement of particle swam optimization algorithm. This algorithm maintained the colony multiplicity, reinforced the PSO algorithm of global optimization, advanced computational precision, and improved the convergence property and robustness. Avoided algorithm stagnancy happed in more degree, so this algorithm was a availability of optimization.
Keywords: Hybrid optimization algorithm Chaos optimization algorithm Particle swam optimization algorithm
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No.1112794249117124****
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