Differential evolution with adaptive mechanism of population size according to current population diversity

2019 ◽  
Vol 50 ◽  
pp. 100519 ◽  
Author(s):  
Radka Poláková ◽  
Josef Tvrdík ◽  
Petr Bujok
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Population Diversity ◽  
Adaptive Mechanism ◽  
Current Population

scholarly journals The effect of change in population size on DNA polymorphism.

Genetics ◽  
1989 ◽  
Vol 123 (3) ◽  
pp. 597-601 ◽  
Author(s):  
F Tajima
Keyword(s):  
Population Size ◽  
Dna Sequences ◽  
Dna Polymorphism ◽  
Population Bottleneck ◽  
Expected Number ◽  
Original Population ◽  
Current Population ◽  
Segregating Sites

Abstract The expected number of segregating sites and the expectation of the average number of nucleotide differences among DNA sequences randomly sampled from a population, which is not in equilibrium, have been developed. The results obtained indicate that, in the case where the population size has changed drastically, the number of segregating sites is influenced by the size of the current population more strongly than is the average number of nucleotide differences, while the average number of nucleotide differences is affected by the size of the original population more severely than is the number of segregating sites. The results also indicate that the average number of nucleotide differences is affected by a population bottleneck more strongly than is the number of segregating sites.

scholarly journals An Adaptive Mechanism with Cooperative Coevolution and Covariance for Differential Evolution

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bin Wang ◽  
Lu Ren ◽  
Juan Diego Prado ◽  
Wenjuan He ◽  
Haiyan Jin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Adaptive Mechanism ◽  
Cooperative Coevolution

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 Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1565 ◽  
Author(s):  
Xingping Sun ◽  
Linsheng Jiang ◽  
Yong Shen ◽  
Hongwei Kang ◽  
Qingyi Chen
Keyword(s):  
Differential Evolution ◽  
Clustering Analysis ◽  
Numerical Optimization ◽  
Optimization Algorithms ◽  
Search Space ◽  
Population Diversity ◽  
Convergence Of Algorithms ◽  
Adaptive Differential Evolution ◽  
Complex Methods ◽  
Single Objective

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

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