An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms f...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 18; H. 4; S. 577 - 601
Hauptverfasser:	Deb, Kalyanmoy, Jain, Himanshu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.08.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science; control theory; systems Constraints Educational institutions Evolutionary Evolutionary algorithms evolutionary computation Exact sciences and technology Handling large dimension Many-objective optimization Measurement multi-criterion optimization non-dominated sorting NSGA-III Optimization Optimization algorithms Production methods Recognition Sociology Statistics Theoretical computing Vectors Zirconium large dimension many-objective optimization NSGA-III nondominated sorting multicriterion optimization Evolutionary computation Non dominating set Large dimension Multicriteria analysis Evolutionary algorithm Multiobjective programming Optimization Sorting Problem solving Mathematical programming
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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