基于模拟退火机制的多种群萤火虫算法

doi:10.11772/j.issn.1001-9081.2015.03.691

计算机应用 ›› 2015, Vol. 35 ›› Issue (3): 691-695.DOI: 10.11772/j.issn.1001-9081.2015.03.691

基于模拟退火机制的多种群萤火虫算法

王铭波¹, 符强^1,2, 童楠¹, 刘政¹, 赵一鸣¹

1. 宁波大学科学技术学院, 浙江宁波 315212;
2. 宁波大学信息科学与工程学院, 浙江宁波 315211

收稿日期:2014-10-11 修回日期:2014-11-23 出版日期:2015-03-10 发布日期:2015-03-13
通讯作者: 符强
作者简介:王铭波(1994-),男,浙江宁波人,主要研究方向:智能优化算法;符强(1975-),男,江西赣州人,讲师,博士研究生,主要研究方向:集成电路设计自动化、智能优化算法;童楠(1981-),女,浙江绍兴人,讲师,硕士,主要研究方向:智能控制与算法优化、数据挖掘
基金资助:
浙江省教育厅科研项目(Y201326770);浙江省大学生新苗人才计划项目(2014R405066);十二五浙江省重点学科建设项目(科[2012]80-314);宁波市自然科学基金资助项目(2014A610069)

Multi-group firefly algorithm based on simulated annealing mechanism

WANG Mingbo¹, FU Qiang^1,2, TONG Nan¹, LIU Zheng¹, ZHAO Yiming¹

1. College of Science and Technology, Ningbo University, Ningbo Zhejiang 315212, China;
2. Faculty of Information Science and Engineering, Ningbo University, Ningobo Zhejiang 315211, China

Received:2014-10-11 Revised:2014-11-23 Online:2015-03-10 Published:2015-03-13

摘要/Abstract

摘要：

针对传统萤火虫算法(FA)中存在的过早收敛和易陷入局部最优解等问题,提出了一种基于模拟退火机制的多种群萤火虫算法(MFA_SA):将萤火虫种群平均分为参数不同的多个子种群。为了防止算法陷入局部最优解,利用模拟退火机制大概率接受较好的解,小概率接受较差的解。同时,在种群寻优的过程中引入可变的距离权重,通过萤火虫算法的迭代次数动态调整萤火虫的"视野"范围。利用5个标准测试函数对该算法进行了对比仿真测试,结果表明,该算法在4个测试函数中均能寻找到全局最优解,并且在最优值、平均值、方差等指标上均比对比算法高出多个数量级,验证了新算法的有效性。

关键词: 萤火虫算法, 模拟退火机制, 多种群, 距离权重

Abstract:

According to the problem of premature convergence and local optimum in Firefly Algorithm (FA), this paper came up with a kind of multi-group firefly algorithm based on simulated annealing mechanism (MFA_SA), which equally divided firefly populations into many child populations with different parameter. To prevent algorithm fall into local optimum, simulated annealing mechanism was adopted to accept good solutions by the big probability, and keep bad solutions by the small probability. Meanwhile, variable distance weight was led into the process of population optimization to dynamically adjust the "vision" of firefly individual. Experiments were conducted on 5 kinds of benchmark functions between MFA_SA and three comparison algorithms. The experimental results show that, MFA_SA can find the global optimal solutions in 4 testing function, and achieve much better optimal solution, average and variance than other comparison algorithms. which demonstrates the effectiveness of the new algorithm.

Key words: Firefly Algorithm (FA), simulated annealing mechanism, multi-group, distance weight

中图分类号:

TP183

王铭波, 符强, 童楠, 刘政, 赵一鸣. 基于模拟退火机制的多种群萤火虫算法[J]. 计算机应用, 2015, 35(3): 691-695.

WANG Mingbo, FU Qiang, TONG Nan, LIU Zheng, ZHAO Yiming. Multi-group firefly algorithm based on simulated annealing mechanism[J]. Journal of Computer Applications, 2015, 35(3): 691-695.

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基于模拟退火机制的多种群萤火虫算法

Multi-group firefly algorithm based on simulated annealing mechanism

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