Multi-objective optimization using genetic algorithms: A tutorial

Abdullah Konak, David W. Coit, Alice E. Smith

Research output: Contribution to journalArticlepeer-review

2058 Scopus citations

Abstract

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.

Original languageEnglish (US)
Pages (from-to)992-1007
Number of pages16
JournalReliability Engineering and System Safety
Volume91
Issue number9
DOIs
StatePublished - Sep 1 2006

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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