Advances in Evolutionary Multi-objective Optimization
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  • 作者：Kalyanmoy Deb (1) kdeb@egr.msu.edu
  • 关键词：Evolutionary optimization – ; Multi ; objective optimization – ; Evolutionary multi ; objective optimization – ; EMO
  • 刊名：Lecture Notes in Computer Science
  • 出版年：2012
  • 出版时间：2012
  • 年：2012
  • 卷：7515
  • 期：1
  • 页码：1-26
  • 全文大小：680.6 KB
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  • 作者单位：1. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
  • ISSN：1611-3349
文摘
Started during 1993-95 with three different algorithms, evolutionary multi-objective optimization (EMO) has come a long way in a quick time to establish itself as a useful field of research and application. Till to date, there exist numerous textbooks and edited books, commercial softwares dedicated to EMO algorithms, freely downloadable codes in most-used computer languages, a biannual conference series (called EMO conference series) running successfully since 2001, and special sessions and workshops held in almost all major evolutionary computing conferences. In this paper, we discuss briefly the principles of EMO through an illustration of one specific algorithm.Thereafter, we focus on mentioning a few recent research and application developments of EMO. Specifically, we discuss EMO’s use with multiple criterion decision making (MCDM) procedures and EMO’s applicability in handling of a large number of objectives. Besides, the concept of multi-objectivization and innovization – which are practically motivated, is discussed next. A few other key advancements are also highlighted. The development and application of EMO to multi-objective optimization problems and their continued extensions to solve other related problems have elevated the EMO research to a level which may now undoubtedly be termed as an active field of research with a wide range of theoretical and practical research and application opportunities. EMO concepts are ready to be applied to search based software engineering (SBSE) problems.

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