Parallel compact differential evolution for optimization applied to image segmentation

Xiao Sui, Shu Chuan Chu, Jeng Shyang Pan, Hao Luo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.

Original languageEnglish
Article number2195
Number of pages22
JournalApplied Sciences (Switzerland)
Volume10
Issue number6
DOIs
Publication statusPublished - 24 Mar 2020

Bibliographical note

c 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Keywords

  • Communication strategies
  • Compact differential evolution (cde)
  • Image segmentation
  • Parallel compact differential evolution (pcde)

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