A quantum differential evolution algorithm for function optimization

2010 ◽  
Author(s):  
Qiuyan Xu ◽  
Jun Guo
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Evolution Algorithm

An Efficient Differential Evolution Algorithm For Function Optimization

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

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
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Function Optimization ◽  
Multimodal Optimization ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
Optimal Value ◽  
Evolution Algorithm

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.

A Hybrid Differential Evolution Algorithm for Solving Function Optimization

2010 ◽  
Vol 439-440 ◽  
pp. 315-320
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Differential Evolution ◽  
Novel Mutation ◽  
Experimental Studies ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Test Cases ◽  
Key Points ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Mutation Strategies

One of the key points resulting in the success of differential evolution (DE) is its mechanism of different mutation strategies for generating mutant vectors. In this paper, we also present a novel mutation strategy inspired by the velocity updating scheme of particle swarm optimization (PSO). The proposed approach is called HDE, which conducts the mutation strategy on the global best vector for each generation. Experimental studies on 8 well-known benchmark functions show that HDE outperforms other three compared DE algorithms in most test cases.

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