基于Cat混沌与高斯变异的改进灰狼优化算法

发布时间：2018-01-16 14:00

本文关键词：基于Cat混沌与高斯变异的改进灰狼优化算法　出处：《计算机工程与应用》2017年04期 　论文类型：期刊论文

【摘要】：针对基本灰狼优化算法在求解复杂问题时同样存在依赖初始种群、过早收敛、易陷入局部最优等缺点,提出一种改进的灰狼优化算法应用于求解函数优化问题中。该算法首先利用混沌Cat映射产生灰狼种群的初始位置,为算法全局搜索过程的种群多样性奠定基础;同时引入粒子群算法中的个体记忆功能以便增强算法的局部搜索能力和加快其收敛速度;最后采用高斯变异扰动和优胜劣汰选择规则对当前最优解进行变异操作以避免算法陷入局部最优。对13个基准测试函数进行仿真实验,结果表明,与基本GWO算法、PSO算法、GA算法以及ACO算法相比,该算法具有更好的求解精度和更快的收敛速度。
[Abstract]:In order to solve the complex problems, the basic gray wolf optimization algorithm also has the disadvantages of depending on the initial population, converging prematurely and falling into local optimum easily. An improved gray wolf optimization algorithm is proposed to solve the function optimization problem. Firstly, chaotic Cat mapping is used to generate the initial position of the gray wolf population. It lays a foundation for the diversity of population in the global search process of the algorithm. At the same time, the individual memory function of PSO is introduced in order to enhance the local search ability and speed up the convergence of PSO. Finally, Gao Si mutation disturbance and survival of the fittest rules are used to operate the current optimal solution to avoid the algorithm falling into local optimum. 13 benchmark functions are simulated and the results show that. Compared with the basic GWO algorithm and the ACO algorithm, the algorithm has better accuracy and faster convergence speed.
【作者单位】：广西大学电气工程学院;广西大学计算机与电子信息学院;
【基金】：国家自然科学基金(No.61364007,No.61462006) 广西自然科学基金(No.2014GXNSFAA118391)
【分类号】：TP18
【正文快照】： 1引言灰狼优化[1(]Grey Wolf Optimization,GWO)算法是继粒子群算法[2]、人工蜂群算法[3]和引力搜索算法[4]后提出的又一新颖的群智能优化搜索算法。相比于粒子群算法、人工蜂群算法和果蝇优化算法,该算法通过对狼群进行了种群等级划分并模拟其捕食策略,指导群体搜索最优值,

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