Super-fit and population size reduction in compact Differential Evolution

2011 ◽  
Author(s):  
Giovanni Iacca ◽  
Rammohan Mallipeddi ◽  
Ernesto Mininno ◽  
Ferrante Neri ◽  
Pannuthurai Nagaratnam Suganthan
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
Size Reduction

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 Improved ensemble of differential evolution variants

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256206
Author(s):  
Juan Yao ◽  
Zhe Chen ◽  
Zhenling Liu
Keyword(s):  
Differential Evolution ◽  
Population Size ◽  
State Of The Art ◽  
Population Based ◽  
Size Reduction ◽  
Real Parameter ◽  
De Algorithm ◽  
External Archive ◽  
Single Objective ◽  
Event Triggered

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.

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