A differential evolution algorithm based on individual-sorting and individual-sampling strategies

Yang LOU, Junli LI*, Gang LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)

Abstract

In this paper we propose a novel hybrid version of Differential Evolution (DE). Firstly we modify the traditional structure of population in DE and propose a new strategy for population setting, in which the population is sorted in line with the fitness values of individuals. Another method is saltatory sampling with a nonrandom order, which is utilized to select candidates for the mutation operation. Furthermore, a strategy of survival of the fittest was used for individual selection operation. Combined the two strategies with DE, differential evolution based on individual-sorting and individual-sampling (ISSDE) is proposed, via testing on a series benchmark functions and compared with three variants of DE, the simulation results show that the proposed ISSDE has a better performance both in convergence speed and robustness. 1553-9105/

Original languageEnglish
Pages (from-to)717-725
Number of pages9
JournalJournal of Computational Information Systems
Volume8
Issue number2
Publication statusPublished - Jan 2012
Externally publishedYes

Bibliographical note

This research was supported by the Natural Science Foundation of Zhejiang Province under Grant No. Y1100076, K. C. Wong Magna Fund in Ningbo University, and the Scientific Research Foundation of Graduate School of Ningbo University.

Keywords

  • Differential evolution
  • Individual-sampling
  • Individual-sorting
  • Sampling
  • Sorting

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