Controlling Population Size in Differential Evolution by Diversity Mechanism

2017 ◽  
pp. 408-417 ◽  
Author(s):  
Radka Poláková
Keyword(s):  
Differential Evolution ◽  
Population Size

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.

scholarly journals L-SHADE with Alternative Population Size Reduction for Unconstrained Continuous Optimization

2020 ◽  
Author(s):  
Christopher Renkavieski ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Numerical Optimization ◽  
Continuous Optimization ◽  
Size Reduction

Differential Evolution (DE) is a powerful and versatile algorithmfor numerical optimization, but one of its downsides is its numberof parameters that need to be tuned. Multiple techniques have beenproposed to self-adapt DE’s parameters, with L-SHADE being oneof the most well established in the literature. This work presentsthe A-SHADE algorithm, which modifies the population size reductionschema of L-SHADE, and also EB-A-SHADE, which applies amutation strategy hybridization framework to A-SHADE. Thesealgorithms are applied to the CEC2013 benchmark set with 100dimensions, and it’s shown that A-SHADE and EB-A-SHADE canachieve competitive results.

scholarly journals The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1909
Author(s):  
Petr Bujok
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Real Life ◽  
Efficient Solutions ◽  
Proper Position ◽  
The Real ◽  
Constrained Problems ◽  
De Algorithm ◽  
Real World Application ◽  
Euclidean Distances

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.

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