Multi-objective differential evolution with self-navigation

Ke Li, Sam Kwong, Ran Wang, Jingjing Cao, Imre J. Rudas

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

15 Citations (Scopus)

Abstract

Traditional differential evolution (DE) mutation operators explore the search space with no considering the information about the search directions, which results in a purely stochastic behavior. This paper presents a DE variant with self-navigation ability for multi-objective optimization (MODE/SN). It maintains a pool of well designed DE mutation operators with distinct search behaviors and applies them in an adaptive way according to the feedback information from the optimization process. Moreover, we deploy the neural network, which is trained by the extreme learning machine, for mapping an artificially generated solution in the objective space back into the decision space. Empirical results demonstrate that MODE/SN outperforms several state-of-the-art algorithms on a set of benchmark problems with variable linkages. © 2012 IEEE.
Original languageEnglish
Title of host publicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages508-513
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 14 Oct 201217 Oct 2012

Publication series

Name
ISSN (Print)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
PlaceKorea, Republic of
CitySeoul
Period14/10/1217/10/12

Research Keywords

  • Differential evolution
  • multi-objective evolutionary algorithm (MOEA)
  • neural network

Fingerprint

Dive into the research topics of 'Multi-objective differential evolution with self-navigation'. Together they form a unique fingerprint.

Cite this