scholarly journals Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Xu Wang ◽  
Shuguang Zhao
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Control Parameters ◽  
De Algorithm ◽  
Wide Range ◽  
Evolution Algorithm ◽  
Mutation Strategies ◽  
Nonparametric Statistical ◽  
Nonparametric Statistical Procedures ◽  
Self Adaptive

A differential evolution (DE) algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.

scholarly journals SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

2021 ◽  
Author(s):  
Qingtao Pan ◽  
Jun Tang ◽  
Haoran Wang ◽  
Hao Li ◽  
Xi Chen ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Statistical Tests ◽  
Differential Evolution Algorithm ◽  
Control Parameters ◽  
Simulation Experiments ◽  
Adaptive Differential Evolution ◽  
Shuffled Frog Leaping ◽  
Evolution Algorithm ◽  
Nonparametric Statistical ◽  
Self Adaptive

AbstractThe differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.

SACPDE: Self-Adaptive Control Parameters in Differential Evolution Algorithm for Antenna Design Applications

2021 ◽  
pp. 575-589
Author(s):  
Danilo F. Poveda-Pulla ◽  
Jefferson V. Dominguez-Paute ◽  
Luis F. Guerrero-Vásquez ◽  
Paúl A. Chasi-Pesántez ◽  
Jorge O. Ordoñez-Ordoñez ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Antenna Design ◽  
Control Parameters ◽  
Evolution Algorithm ◽  
Self Adaptive

scholarly journals An Improved Differential Evolution Algorithm Based on Adaptive Parameter

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Differential Evolution ◽  
Optimization Technique ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Control Parameters ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Evolution Stage ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

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