萤火虫优化算法Matlab程序设计  

Matlab Programming of Glowworm Swarm Optimization Algorithm

作　　者：张玉丽[1] 刘朝美[1] ZHANG Yu-li;LIU Chao-mei(College of Science,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学理学院,辽宁大连116028

出　　处：《软件导刊》2020年第9期132-135,共4页Software Guide

基　　金：辽宁省博士科研启动基金项目(201601247);辽宁省教育厅项目(JDL2019026)。

摘　　要：萤火虫优化算法(GSO)是一种计算多模函数多峰值问题的群智能算法,由模拟自然界中萤火虫发光的生物学特征发展而来。在GSO算法中,萤火虫根据自适应的感应决策范围寻找比自身荧光素高的萤火虫,并通过概率选择机制朝其运动,以实现寻优目的。简要阐述GSO算法基本原理,对算法各个参数进行分析说明,利用Matlab软件构建GSO算法在整个寻优过程中的可视化环境,并给出算法源代码。仿真实验首先实现了自适应感应决策范围更新过程,然后通过多模函数仿真示例测试了该方法的有效性,从而实现了利用萤火虫算法解决多模函数多峰值优化问题。Glowworm swarm optimization(GSO)algorithm is a swarm intelligence based algorithm for computing multiple optima of multimodal functions. In the GSO algorithm,glowworms look for glowworms with higher fluorescein according to the adaptive sensing decision range,and move toward them through a probability selection mechanism to achieve the goal of optimization. The fundamentals of GSO is briefly introduced,and the parameters of GSO are analyzed and explained. Complete programming of GSO is provided in detail and the visual environment of the whole 0 ptimization process of GSO is constructed by Matlab. Firstly,the range updating process of adaptive neighborhood range is realized in the simulation experiment. In addition,simulation experiments on two standard multimodal functions are carried out,and the results show that GSO algorithm performs very well in capturing multiple optima of multimodal functions.

关 键 词：萤火虫优化算法 MATLAB 多模函数优化 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]