Abstract
Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.
Keywords
Variety (cybernetics)Evolutionary algorithmMulti-objective optimizationComputer scienceMathematical optimizationRanking (information retrieval)Class (philosophy)Focus (optics)Pareto principleMachine learningArtificial intelligenceMathematics
MeSH Terms
AlgorithmsAnimalsBiological EvolutionComputer SimulationFemaleMaleModelsGeneticStochastic Processes
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Publication Info
- Year
- 2000
- Type
- review
- Volume
- 8
- Issue
- 2
- Pages
- 125-147
- Citations
- 1179
- Access
- Closed
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Cite This
David A. Van Veldhuizen,
Gary B. Lamont
(2000).
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art.
Evolutionary Computation
, 8
(2)
, 125-147.
https://doi.org/10.1162/106365600568158
Identifiers
- DOI
- 10.1162/106365600568158
- PMID
- 10843518