免疫双向蛙跳算法及其在多峰函数优化中的应用  

Immune Bidirectional Frog Leaping Algorithm and Its Application in Multi-modal Function Optimization

作　　者：吴建辉 杨永舒[1] 陈华 张小刚 WU Jianhui;YANG Yongshu;CHEN Hua;ZHANG Xiaogang(College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China;School of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学信息科学与工程学院,长沙410082 [2]湖南大学电气与信息工程学院,长沙410082

出　　处：《计算机工程》2018年第9期184-191,共8页Computer Engineering

基　　金：国家自然科学基金(61673162;61672216)

摘　　要：混合蛙跳算法在多模态函数寻优中存在易陷入局部最优、求解精度较低、寻优峰值数过少等问题。为此,提出一种基于圆内衍生变异的免疫双向蛙跳算法。在每次全局循环迭代中,通过基于双向进化机制的混合蛙跳算法,以模因组的形式进行局部-全局搜索,根据双控限幅变异的克隆选择算法,对已搜索到的较优解进行局部优化,以提高解的精度,将部分函数旋转,从而验证算法性能。仿真结果表明,与原始蛙跳算法相比,在保证收敛速度的同时,该算法的寻优精度和搜索到的极值点数目均有明显提高。In order to overcome the shortcomings of Shuffled Frog Leaping Algorithm(SFLA)in solving multi-modal function optimization problems which includes easily falling into local minimum,low accuracy and difficultly searching extreme points as many as possible,a novel Bidirectional Immune Shuffled Frog Leaping Algorithm(BISFLA)based on circle derived mutation is proposed.This algorithm in each loop iteration employs the form of local-global based on bidirectional evolution mechanism to searching in the feasible zone,and then uses double Control Mutative Clonal Selection Algorithm(DCSA)to improve the accuracy of the sub-optimal solution.The function is rotated to further verify the performance of the algorithm.Simulation results show that compared with the original frog leaping algorithm,the optimization accuracy of the algorithm and the number of extremum points are significantly improved while the convergence speed is guaranteed.

关 键 词：混合蛙跳算法 克隆选择算法 多峰函数 免疫系统 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]