计算机工程与应用2017，Vol.53Issue(4)：1-9,50,10.DOI:10.3778/j.issn.1002-8331.1607-0244
基于Cat混沌与高斯变异的改进灰狼优化算法
Improved grey wolf optimization algorithm based on chaotic Cat mapping and Gaussian mutation
摘要
Abstract
In order to overcome the defects of basic grey wolf optimization algorithm about the dependence on the initial population, the presence of premature convergence, and easily getting into local minima, this paper proposes an improved grey wolf optimization algorithm applied to solve the function optimization problem. In the proposed algorithm, firstly, chaotic Cat sequence is used to initiate individual position, which can strengthen the diversity of global searching. Secondly, the individual memory from PSO are applied to enhance the local search ability and convergence speed. Thirdly, a Gaussian disturbance based the rules of survival of the fittest will be given on the global optimum of each generation, thus the algo-rithm can effectively jump out of local minima. Experimental results based on the thirteen benchmark functions show that the proposed improved GWO algorithm is superior to the basic GWO, PSO, GA and ACO algorithm in both computational accuracy and convergence rate.关键词
混沌cat映射/灰狼优化算法/函数优化/高斯变异/优胜劣汰选择Key words
chaotic Cat map/grey wolf optimization algorithm/function optimization/Gaussian mutation/the rules of survival of the fittest分类
信息技术与安全科学引用本文复制引用
徐辰华,李成县,喻昕,黄清宝..基于Cat混沌与高斯变异的改进灰狼优化算法[J].计算机工程与应用,2017,53(4):1-9,50,10.基金项目
国家自然科学基金(No.61364007,No.61462006) （No.61364007,No.61462006）
广西自然科学基金(No.2014GXNSFAA118391). （No.2014GXNSFAA118391）
