The cellular differential evolution based on chaotic local search
type: Item
instance of: scholarly article
title: The cellular differential evolution based on chaotic local search
zbMATH Open document ID: 1394.90564
DOI: 10.1155/2015/128902
author: Qingfeng Ding
author: Guoxin Zheng
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: To avoid immature convergence and tune the selection pressure in the differential evolution (DE) algorithm, a new differential evolution algorithm based on cellular automata and chaotic local search (CLS) or ccDE is proposed. To balance the exploration and exploitation tradeoff of differential evolution, the interaction among individuals is limited in cellular neighbors instead of controlling parameters in the canonical DE. To improve the optimizing performance of DE, the CLS helps by exploring a large region to avoid immature convergence in the early evolutionary stage and exploiting a small region to refine the final solutions in the later evolutionary stage. What is more, to improve the convergence characteristics and maintain the population diversity, the binomial crossover operator in the canonical DE may be instead by the orthogonal crossover operator without crossover rate. The performance of ccDE is widely evaluated on a set of 14 bound constrained numerical optimization problems compared with the canonical DE and several DE variants. The simulation results show that ccDE has better performances in terms of convergence rate and solution accuracy than other optimizers.
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6925528
Wikidata QID: Q59117319
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/128902
OpenAlex ID: W1571195126
cites work: Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
description: scientific article; zbMATH DE number 6925528