Novel objective-space-dividing multi-objectives evolutionary algorithm and its convergence property

Zhi Yong Li, Chao Chen, Chang An Ren, Esraa M. Mohammed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

To overcome the shortcomings of Multi-Objectives Evolutionary Algorithms (MOEAs) based on the notion of Objective-Space-Dividing (OSD) with high calculation complexity, this paper proposes an improved algorithm called OSD-MOEA. The proposed algorithm supports the following features: 1) transforming the Pareto relationship among individuals to the ranking relationship of the total value of indexes in divided space; 2) simple and efficient environment choosing method based on index ranking; 3) an individual crowding algorithm which rapidly chooses the nearest individual to the origin. Convergence analysis shows the convergence property of the proposed algorithm. Simulation results of the proposed algorithm OSD-MOEA are compared with NSGAII and PSFGA and high efficiency, low time complexity and good convergence are noticed.

Original languageEnglish
Title of host publicationProceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing
Subtitle of host publicationTheories and Applications, BIC-TA 2010
Pages372-379
Number of pages8
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010 - Changsha, China
Duration: 23 Sep 201026 Sep 2010

Publication series

NameProceedings 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

Conference

Conference2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010
Country/TerritoryChina
CityChangsha
Period23/09/1026/09/10

Keywords

  • Evolutionary algorithms
  • Multi-objectives optimization
  • Objective-space- dividing

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