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 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.

Differential Evolution for the Optimization of DMSO-Free Cryoprotectants: Influence of Control Parameters

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Chia-Hsing Pi ◽  
Peter I. Dosa ◽  
Allison Hubel
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Differential Evolution Algorithm ◽  
Training Model ◽  
Jurkat Cells ◽  
Dimensional Structure ◽  
Classical Type ◽  
Control Parameters ◽  
Evolution Algorithm ◽  
Self Adaptive

Abstract This study presents the influence of control parameters including population (NP) size, mutation factor (F), crossover (Cr), and four types of differential evolution (DE) algorithms including random, best, local-to-best, and local-to-best with self-adaptive (SA) modification for the purpose of optimizing the compositions of dimethylsufloxide (DMSO)-free cryoprotectants. Post-thaw recovery of Jurkat cells cryopreserved with two DMSO-free cryoprotectants at a cooling rate of 1 °C/min displayed a nonlinear, four-dimensional structure with multiple saddle nodes, which was a suitable training model to tune the control parameters and select the most appropriate type of differential evolution algorithm. Self-adaptive modification presented better performance in terms of optimization accuracy and sensitivity of mutation factor and crossover among the four different types of algorithms tested. Specifically, the classical type of differential evolution algorithm exhibited a wide acceptance to mutation factor and crossover. The optimization performance is more sensitive to mutation than crossover and the optimization accuracy is proportional to the population size. Increasing population size also reduces the sensitivity of the algorithm to the value of the mutation factor and crossover. The analysis of optimization accuracy and convergence speed suggests larger population size with F > 0.7 and Cr > 0.3 are well suited for use with cryopreservation optimization purposes. The tuned differential evolution algorithm is validated through finding global maximums of other two DMSO-free cryoprotectant formulation datasets. The results of these studies can be used to help more efficiently determine the optimal composition of multicomponent DMSO-free cryoprotectants in the future.

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