A differential evolution algorithm with intersect mutation operator

2013 ◽  
Vol 13 (1) ◽  
pp. 390-401 ◽  
Author(s):  
Yinzhi Zhou ◽  
Xinyu Li ◽  
Liang Gao
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Mutation Operator ◽  
Evolution Algorithm

Differential evolution algorithm with fitness and diversity ranking-based mutation operator

2021 ◽  
Vol 61 ◽  
pp. 100816
Author(s):  
Jianchao Cheng ◽  
Zhibin Pan ◽  
Hao Liang ◽  
Zhaoqi Gao ◽  
Jinghuai Gao
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Mutation Operator ◽  
Evolution Algorithm

scholarly journals Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning

2019 ◽  
Vol 37 (5) ◽  
pp. 1000-1010
Author(s):  
Wenhai Wu ◽  
Xiaofeng Guo ◽  
Siyu Zhou ◽  
Jintao Liu
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Simple Structure ◽  
Differential Evolution Algorithm ◽  
Initial Population ◽  
Mutation Operator ◽  
Competition Mechanism ◽  
Opposition Based Learning ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

Differential evolution is a global optimization algorithm based on greedy competition mechanism, which has the advantages of simple structure, less control parameters, higher reliability and convergence. Combining with the constraint-handling techniques, the constraint optimization problem can be efficiently solved. An adaptive differential evolution algorithm is proposed by using generalized opposition-based learning (GOBL-ACDE), in which the generalized opposition-based learning is used to generate initial population and executes the generation jumping. And the adaptive trade-off model is utilized to handle the constraints as the improved adaptive ranking mutation operator is adopted to generate new population. The experimental results show that the algorithm has better performance in accuracy and convergence speed comparing with CDE, DDE, A-DDE and. And the effect of the generalized opposition-based learning and improved adaptive ranking mutation operator of the GOBL-ACDE have been analyzed and evaluated as well.

Diversifying Search Moves in a Shuffled Differential Evolution Algorithm

2016 ◽  
Vol 15 (01) ◽  
pp. 1650002
Author(s):  
Morteza Alinia Ahandani ◽  
Seyyed Sadegh Bibak Sareshkeh
Keyword(s):  
Differential Evolution ◽  
Spread Spectrum ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Code Design ◽  
Mutation Operator ◽  
Secondary Mutation ◽  
New Mutation ◽  
Phase Code ◽  
Evolution Algorithm

The shuffled differential evolution (SDE) is a recently proposed version of differential evolution (DE). However the SDE employs several efforts to compensate limited amount of search moves in the original DE, but these efforts are performed by the same operator. To vary search moves of the SDE, this research proposes employing a secondary mutation operator beside of first mutation operator. This new mutation operator can generate some different offspring than those generated by the first one. Experiments demonstrate a better performance of the proposed algorithm than the SDE. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE and other evolutionary algorithms reported in the literature confirm a better or at least a competitive performance of our proposed algorithm. Also a real-world optimization problem, namely, Spread Spectrum Radar Polly phase Code Design, is solved to show the wide applicability of the DSDE.

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