scholarly journals Review and analysis of three components of the differential evolution mutation operator in MOEA/D-DE

2019 ◽  
Vol 23 (23) ◽  
pp. 12843-12857 ◽  
Author(s):  
Ryoji Tanabe ◽  
Hisao Ishibuchi
Keyword(s):  
Differential Evolution ◽  
Mutation Operator ◽  
Three Components

A new parameter setting-based modified differential evolution for function optimization

2020 ◽  
Vol 11 (04) ◽  
pp. 2050029
Author(s):  
Sukanta Nama ◽  
Apu Kumar Saha
Keyword(s):  
Performance Evaluation ◽  
Differential Evolution ◽  
Real World ◽  
Population Based ◽  
Function Optimization ◽  
Mutation Operator ◽  
Control Parameters ◽  
Test Results ◽  
Parameter Setting ◽  
De Algorithm

The population-based efficient iterative evolutionary algorithm (EA) is differential evolution (DE). It has fewer control parameters but is useful when dealing with complex problems of optimization in the real world. A great deal of progress has already been made and implemented in various fields of engineering and science. Nevertheless, DE is prone to the setting of control parameters in its performance evaluation. Therefore, the appropriate adjustment of the time-consuming control parameters is necessary to achieve optimal DE efficiency. This research proposes a new version of the DE algorithm control parameters and mutation operator. For the justifiability of the suggested method, several benchmark functions are taken from the literature. The test results are contrasted with other literary algorithms.

Cellular Differential Evolution for Optimization

2011 ◽  
Vol 474-476 ◽  
pp. 1770-1775
Author(s):  
Gui Wu Hu ◽  
Xiao Yong Du
Keyword(s):  
Cellular Automata ◽  
Differential Evolution ◽  
Information Exchange ◽  
Cellular Structure ◽  
Mutation Operator ◽  
Solution Quality ◽  
Base Vector ◽  
Quality Stability ◽  
Simulation Results ◽  
And Diffusion

This paper is to illustrate the Cellular Differential Evolution with the cellular structure originated from Cellular automata. Cellular neighbor local search has been designed; base vector or global best in mutation operator is substituted by neighborhood-best, which overcomes the weakness of single selection relating to global best, and balances the contradiction of local and global search, and improves the diversity of population. In addition, cellular structure ensures information exchange, inheritance and diffusion. Finally, a specific algorithm has been implemented: compared with similar variants of DE, the simulation results on 9 benchmark functions demonstrate that cellular differential evolutions are provided with obvious advantages in the solution-quality, stability and speed. <b></b>

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