An Adaptive Cauchy Differential Evolution Algorithm with Population Size Reduction and Modified Multiple Mutation Strategies

2015 ◽  
pp. 13-26 ◽  
Author(s):  
Tae Jong Choi ◽  
Chang Wook Ahn
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Differential Evolution Algorithm ◽  
Size Reduction ◽  
Multiple Mutation ◽  
Evolution Algorithm ◽  
Mutation Strategies

scholarly journals An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Wan-li Xiang ◽  
Xue-lei Meng ◽  
Mei-qing An ◽  
Yin-zhen Li ◽  
Ming-xia Gao
Keyword(s):  
Differential Evolution ◽  
Linear Time ◽  
Differential Evolution Algorithm ◽  
Premature Convergence ◽  
Perturbation Scheme ◽  
Solution Quality ◽  
Multiple Mutation ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Mutation Strategies

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

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