基于改进型进化机制的萤火虫优化算法

计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 228-231.

基于改进型进化机制的萤火虫优化算法

符强,童楠,钟才明,赵一鸣

宁波大学科学技术学院宁波315212;宁波大学科学技术学院宁波315212;宁波大学科学技术学院宁波315212;宁波大学科学技术学院宁波315212

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受浙江省教育厅科研项目(Y201326872),浙江省自然科学基金项目(Y1090851),宁波大学科研基金项目(XYL12009),十二五浙江省重点学科建设项目(计算机应用技术)资助

Firefly Algorithm Based on Improved Evolutionism

FU Qiang,TONG Nan,ZHONG Cai-ming and ZHAO Yi-ming

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 分析了萤火虫算法的进化计算机制,并利用实例对萤火虫算法中容易发生进化过早停滞的原因进行了研究。提出了一种基于新型进化计算模式的改进型萤火虫优化算法,该算法在进化初期利用种群最优萤火虫激发群中其他个体的寻优能力,在萤火虫相互之间建构了有效的信息交互网络后,各萤火虫将借助各自视觉范围内的更优近邻个体完成后期搜索和进化,当种群陷入局部最优区域时,利用高斯变异改善萤火虫个体的多样性。利用标准测试函数进行了实验分析,结果表明,改进后的萤火虫算法能有效改善过早进化停滞问题。

关键词: 萤火虫算法,群智能,进化机制,高斯变异中图法分类号TP183文献标识码A

Abstract: Analyzing the evolutionary computation mechanism of the Firefly algorithm,a new evolutionary computation model for Firefly algorithm was proposed to solve the evolutionary premature stagnation problem．At the beginning,the fireflies achieve evolution by following the best firefly in global area,and when the mutual system is established among the fireflies for exchanging the information,each firefly is attracted by the brighter glow of other neighboring fireflies．When the population is in local optimization area,Gaussian mutation is used to improve firefly’s diversity．The experiment results of 5classic benchmark functions indicate the feasibility and validity of the improved Firefly algorithm.

Key words: Firefly algorithm(FA),Swarm intelligence,Evolutionism,Gaussian mutation

符强,童楠,钟才明,赵一鸣. 基于改进型进化机制的萤火虫优化算法[J]. 计算机科学, 2014, 41(3): 228-231. https://doi.org/

FU Qiang,TONG Nan,ZHONG Cai-ming and ZHAO Yi-ming. Firefly Algorithm Based on Improved Evolutionism[J]. Computer Science, 2014, 41(3): 228-231. https://doi.org/

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