[1]MO Yuanbin,MA Yanzhui,ZHENG Qiaoyan,et al.Improved firefly algorithm based on simplex method and its application in solving non-linear equation groups[J].CAAI Transactions on Intelligent Systems,2014,9(6):747-755.[doi:10.3969/j.issn.1673-4785.201309075]
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Improved firefly algorithm based on simplex method and its application in solving non-linear equation groups

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 6 Page number: 747-755 Column: 学术论文—智能系统 Public date: 2014-12-25
Title:
Improved firefly algorithm based on simplex method and its application in solving non-linear equation groups
Author(s):
MO Yuanbin1; 2;  MA Yanzhui1;  ZHENG Qiaoyan1;  YUAN Weijun2
1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China;
2. Guangxi Key Laboratory of Mixed Computing Integrated Circuit Design and Analysis, Nanning 530006, China
Keywords:
firefly algorithm; simplex method; function optimization; non-linear equation groups
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.201309075
Abstract:
The firefly algorithm (FA) is a heuristic random optimization algorithm based on groupization. It simulates the social behavior of firefly in the natural environment represented in its biological characteristics of shining. FA has disadvantages in global searching, such as slow convergence speed, high possibility of being trapped in local optimum and low solving precision. An improved FA based on the simplex method is proposed. The proposed method combines the characteristics of speedy local search of simplex method with the global optimization of firefly algorithm. The simplex method modifies the firefly, which is located at poor positions through its reflection, expansion and compression operation. However, it improves the diversity of individuals and avoids falling into local optimum and improves the precision of the algorithm. The results showed that through simulations of standard benchmark functions and nonlinear functions and contrasted with other algorithms, the improved algorithm has a strong advantage in function optimization. It also avoids trapping in local optimum and improves the calculation accuracy to a certain extent.
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