Differential evolution strategies for multi-objective optimization

Ashish M. Gujarathi*, B. V. Babu

*Corresponding author for this work

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

3 Citations (Scopus)


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
Number of pages9
EditionVOL. 1
Publication statusPublished - 2012
Externally publishedYes
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)1867-5662


OtherInternational Conference on Soft Computing for Problem Solving, SocProS 2011


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

ASJC Scopus subject areas

  • Computer Science(all)


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