Evolutionary many-objective optimization: a quick-start guide

Shelvin Chand, Markus Wagner

Research output: Contribution to journalReview ArticleResearchpeer-review

115 Citations (Scopus)

Abstract

Multi-objective optimization problems having more than three objectives are referred to as many-objective optimization problems. Many-objective optimization brings with it a number of challenges that must be addressed, which highlights the need for new and better algorithms that can efficiently handle the growing number of objectives. This article reviews the different challenges associated with many-objective optimization and the work that has been done in the recent-past to alleviate these difficulties. It also highlights how the existing methods and body of knowledge have been used to address the different real world many-objective problems. Finally, it brings focus to some future research opportunities that exist with many-objective optimization.We report in this article what is commonly used, be it algorithms or test problems, so that the reader knows what are the benchmarks and also what other options are available. We deem this to be especially useful for new researchers and for researchers from other fields who wish to do some work in the area of many-objective optimization.

Original languageEnglish
Pages (from-to)35-42
Number of pages8
JournalSurveys in Operations Research and Management Science
Volume20
Issue number2
DOIs
Publication statusPublished - Dec 2015
Externally publishedYes

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