Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization

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

6 Citations (Scopus)

Abstract

Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).

Original languageEnglish
Title of host publicationProceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
Pages933-948
Number of pages16
Publication statusPublished - 2009
Event4th Indian International Conference on Artificial Intelligence, IICAI 2009 - Tumkur, India
Duration: Dec 16 2009Dec 18 2009

Other

Other4th Indian International Conference on Artificial Intelligence, IICAI 2009
CountryIndia
CityTumkur
Period12/16/0912/18/09

Keywords

  • Differential evolution
  • Evolutionary algorithms
  • Multi-objective optimization
  • Optimization
  • Trigonometric mutation

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Gujarathi, A. M., & Babu, B. V. (2009). Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization. In Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009 (pp. 933-948)