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Generalized Differential Evolution for Constrained Multi-Objective Optimization

Generalized Differential Evolution for Constrained Multi-Objective Optimization
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Author(s): Saku Kukkonen (Lappeenranta University of Technology, Finland) and Lampinen Jouni (University of Vaasa, Finland)
Copyright: 2008
Pages: 33
Source title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice
Source Author(s)/Editor(s): Lam Thu Bui (University of New South Wales, Australia) and Sameer Alam (University of New South Wales, Australia)
DOI: 10.4018/978-1-59904-498-9.ch003


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Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many practical problems contain also constraints, which must be taken care of during optimization process. This chapter is about Generalized Differential Evolution, which is a general-purpose optimizer. It is based on a relatively recent Evolutionary Algorithm, Differential Evolution, which has been gaining popularity because of its simplicity and good observed performance. Generalized Differential Evolution extends Differential Evolution for problems with several objectives and constraints. The chapter concentrates on describing different development phases and performance of Generalized Differential Evolution but it also contains a brief review of other multi-objective DE approaches. Ability to solve multi-objective problems is mainly discussed, but constraint handling and the effect of control parameters are also covered. It is found that GDE versions, in particular the latest version, are effective and efficient for solving constrained multi-objective problems.

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