Applying exponential weighting moving average control parameter adaptation technique with generalized differential evolution

2016 ◽  
Author(s):  
Saku Kukkonen ◽  
Carlos A. Coello Coello
Keyword(s):  
Differential Evolution ◽  
Control Parameter ◽  
Moving Average ◽  
Parameter Adaptation ◽  
Exponential Weighting ◽  
Average Control ◽  
Generalized Differential ◽  
Control Parameter Adaptation

scholarly journals Ranking-based hierarchical random mutation in differential evolution

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245887
Author(s):  
Xuxu Zhong ◽  
Meijun Duan ◽  
Peng Cheng
Keyword(s):  
Individual Differences ◽  
Differential Evolution ◽  
Control Parameter ◽  
Population Diversity ◽  
Convergence Speed ◽  
Mutation Operator ◽  
Random Mutation ◽  
Parameter Adaptation ◽  
Fitness Value ◽  
Control Parameter Adaptation

In order to improve the performance of differential evolution (DE), this paper proposes a ranking-based hierarchical random mutation in differential evolution (abbreviated as RHRMDE), in which two improvements are presented. First, RHRMDE introduces a hierarchical random mutation mechanism to apply the classic “DE/rand/1” and its variant on the non-inferior and inferior group determined by the fitness value. The non-inferior group employs the traditional mutation operator “DE/rand/1” with global and random characteristics, which increases the global exploration ability and population diversity. The inferior group uses the improved mutation operator “DE/rand/1” with elite and random characteristics, which enhances the local exploitation ability and convergence speed. Second, the control parameter adaptation of RHRMDE not only considers the complexity differences of various problems but also takes individual differences into account. The proposed RHRMDE is compared with five DE variants and five non-DE algorithms on 32 universal benchmark functions, and the results show that the RHRMDE is superior over the compared algorithms.

GDE4: The Generalized Differential Evolution with Ordered Mutation

2019 ◽  
pp. 101-113 ◽  
Author(s):  
Azam Asilian Bidgoli ◽  
Sedigheh Mahdavi ◽  
Shahryar Rahnamayan ◽  
Hessein Ebrahimpour-Komleh
Keyword(s):  
Differential Evolution ◽  
Generalized Differential

scholarly journals Differential Evolution with Population and Strategy Parameter Adaptation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
V. Gonuguntla ◽  
R. Mallipeddi ◽  
Kalyana C. Veluvolu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Optimization Problems ◽  
Search Space ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
De Algorithm ◽  
Probabilistic Technique ◽  
Number Of Individuals ◽  
Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

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