EMOCA: An evolutionary multi-objective crowding algorithm

Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

A.new evolutionary algorithm is proposed for solving multi-objective optimization problems, focusing on the issue of developing a diverse population of non-dominated solutions. The key new approach in this algorithm is to use a diversity-emphasizing probabilistic approach in determining whether an off-spring individual is considered in the replacement selection phase, along with the use of a non-domination ranking scheme. This evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine bench-mark multi-objective optimization problems and shown to produce non-dominated solutions with significant diversity, outperforming three state-of-the-art multi-objective evolutionary algorithms on most ofthe test problems.

Original languageEnglish (US)
Pages (from-to)107-123
Number of pages17
JournalJournal of Intelligent Systems
Volume17
Issue number1-3
DOIs
StatePublished - Jan 1 2008

Keywords

  • Benchmark test functions
  • Diversity
  • Evolutionary algorithms
  • Multi-objective optimization
  • Non-domination
  • Pareto-optimization

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Artificial Intelligence

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