基于粒子群算法的混洗蛙跳算法  

Shuffled Frog Leaping Algorithm Based on Particle Swarm Optimization

作　　者：栾垚琛[1] 盛建伦[1] 

机构地区：[1]青岛理工大学计算机工程学院,山东青岛266033

出　　处：《计算机与现代化》2009年第11期39-42,共4页Computer and Modernization

摘　　要：基于模因进化的演化算法是一种模拟自然界生物进化或社会种群活动的随机搜索方法。本文介绍一种基于新的智能搜索算法——混洗蛙跳算法的改进演化算法。对SFLA算法和PSO算法的基本原理进行阐述,为了更好地改进SFLA算法局部搜索能力差、收敛速度降低,将粒子群优化算法(PSO)与混洗蛙跳算法(SFLA)相结合,提出一种改进的混洗蛙跳算法(SFLA),能够提高算法的局部搜索能力和稳定性。该算法比上述两种算法具有更好的性能,特别是对函数优化等问题计算效果更好。Evolutionary algorithm is a stochastic search method that mimics natural biological evolution and the social behavior of species. This paper introduces a new intelligence search algorithm based on shuffled frog leaping algorithm. First, this paper in- troduces the basic principle of SFLA and PSO. Second, because of the worse local search of SFLA, and the slow convergence speed, the new algorithm improves the ability of the local search and the steady. It is proved that this proposed algorithm outper- forms the two algorithms previously referenced and has better results for function optimization in particular.

关 键 词：混洗蛙跳算法 粒子群优化算法 函数优化 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]