Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (3): 691-695.

### Multi-group firefly algorithm based on simulated annealing mechanism

1. 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

### 基于模拟退火机制的多种群萤火虫算法

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
• 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)

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.

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