Differential evolution strategies for multi-objective optimization

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

3 Citations (Scopus)

Abstract

Multi-objective optimization (MOO) using evolutionary algorithms has gained popularity in the recent past due to its ability of producing number of solutions in a single run and handling multiple objectives simultaneously. In this effort, several MOO algorithms are developed. In this manuscript several strategies of multi-objective differential evolution algorithm (namely, MODE-I, MODE-III, elitist MODE and hybrid MODE) are briefly discussed. Three important unconstrained test problems are considered for validating the performance (in terms of Pareto front and convergence & diversity metrics) of strategies of MODE algorithm with other popular algorithms from literature. It is observed that the strategies of MODE algorithm are in general able to produce Pareto front with good convergence to the true Pareto front.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011
Pages63-71
Number of pages9
Volume130 AISC
EditionVOL. 1
DOIs
Publication statusPublished - 2012
EventInternational Conference on Soft Computing for Problem Solving, SocProS 2011 - Roorkee, India
Duration: Dec 20 2011Dec 22 2011

Publication series

NameAdvances in Intelligent and Soft Computing
NumberVOL. 1
Volume130 AISC
ISSN (Print)18675662

Other

OtherInternational Conference on Soft Computing for Problem Solving, SocProS 2011
CountryIndia
CityRoorkee
Period12/20/1112/22/11

Fingerprint

Multiobjective optimization
Evolutionary algorithms

Keywords

  • Differential Evolution
  • Evolutionary Algorithms (EAs)
  • Multi-objective Differential Evolution (MODE)
  • Multi-objective optimization (MOO)
  • Pareto front

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Gujarathi, A. M., & Babu, B. V. (2012). Differential evolution strategies for multi-objective optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011 (VOL. 1 ed., Vol. 130 AISC, pp. 63-71). (Advances in Intelligent and Soft Computing; Vol. 130 AISC, No. VOL. 1). https://doi.org/10.1007/978-81-322-0487-9_7

Differential evolution strategies for multi-objective optimization. / Gujarathi, Ashish M.; Babu, B. V.

Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011. Vol. 130 AISC VOL. 1. ed. 2012. p. 63-71 (Advances in Intelligent and Soft Computing; Vol. 130 AISC, No. VOL. 1).

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

Gujarathi, AM & Babu, BV 2012, Differential evolution strategies for multi-objective optimization. in Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011. VOL. 1 edn, vol. 130 AISC, Advances in Intelligent and Soft Computing, no. VOL. 1, vol. 130 AISC, pp. 63-71, International Conference on Soft Computing for Problem Solving, SocProS 2011, Roorkee, India, 12/20/11. https://doi.org/10.1007/978-81-322-0487-9_7
Gujarathi AM, Babu BV. Differential evolution strategies for multi-objective optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011. VOL. 1 ed. Vol. 130 AISC. 2012. p. 63-71. (Advances in Intelligent and Soft Computing; VOL. 1). https://doi.org/10.1007/978-81-322-0487-9_7
Gujarathi, Ashish M. ; Babu, B. V. / Differential evolution strategies for multi-objective optimization. Proceedings of the International Conference on Soft Computing for Problem Solving, SocProS 2011. Vol. 130 AISC VOL. 1. ed. 2012. pp. 63-71 (Advances in Intelligent and Soft Computing; VOL. 1).
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