Niche Differential Evolution Algorithm and its Application in Multimodal Function Optimization

2011 ◽  
Vol 308-310 ◽  
pp. 2431-2435 ◽  
Author(s):  
Na Li ◽  
Yuan Xiang Li ◽  
Zhi Guo Huang ◽  
Yong Wang

In multimodal optimization, the original differential evolution algorithm is easy to duplicate and miss points of the optimal value. To solve this problem, a modified differential evolution algorithm, called niche differential evolution (NDE), is proposed. In the algorithm, the basic differential evolution algorithm is improved based on the niche technology. The rationality to construct the proposed algorithm is discussed. Shubert function, a representative multimodal optimization problem is used to verify the algorithm. The results show that the proposed algorithm can find all global optimum points quickly without strict request for parameters, so it is a good approach to find all global optimum points for multimodal functions.

2013 ◽  
Vol 421 ◽  
pp. 507-511 ◽  
Author(s):  
Nurezayana Zainal ◽  
Azlan Mohd Zain ◽  
Nor Haizan Mohamed Radzi ◽  
Amirmudin Udin

Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.


Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.


2013 ◽  
Vol 12 (3) ◽  
pp. 444-448 ◽  
Author(s):  
Chao-Xue Wang ◽  
Chang-Hua Li ◽  
Hui Dong ◽  
Fan Zhang

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