Elitist-multi-objective differential evolution (E-MODE) algorithm for multi-objective optimization

B. V. Babu, Ashish M. Gujarathi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Several problems in the engineering domain are multi-objective in nature. The solution to multi-objective optimization is a set of solutions rather than a single point solution. Such a set of non-dominated solutions are called Pareto optimal solutions or non-inferior solutions. In this paper, a new algorithm, Elitist-Multi-objective Differential Evolution (E-MODE) is proposed. The proposed algorithm is applied successfully on several test functions, and the results are discussed extensively. Results obtained from the proposed algorithm are compared with those obtained using Multi-objective Differential Evolution (MODE) algorithm. E-MODE is found to give better solutions in terms of wide range of solutions, spread, and diversity of Pareto front than those obtained using MODE.

Original languageEnglish
Title of host publicationProceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007
Pages441-456
Number of pages16
Publication statusPublished - 2007
Externally publishedYes
Event3rd Indian International Conference on Artificial Intelligence, IICAI 2007 - Pune, India
Duration: Dec 17 2007Dec 19 2007

Publication series

NameProceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007

Other

Other3rd Indian International Conference on Artificial Intelligence, IICAI 2007
Country/TerritoryIndia
CityPune
Period12/17/0712/19/07

Keywords

  • Elitist-Multi-objective Differential Evolution (E-MODE)
  • Evolutionary algorithms
  • Evolutionary multi-objective optimization (EMO)
  • Multi-objective Differential Evolution (MODE)
  • Multi-objective optimization (MOO)

ASJC Scopus subject areas

  • Artificial Intelligence

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