Multiobjective evolutionary algorithms: A survey of the state of the art

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Swarm and evolutionary computation Ročník 1; číslo 1; s. 32 - 49
Hlavní autori: Zhou, Aimin, Qu, Bo-Yang, Li, Hui, Zhao, Shi-Zheng, Suganthan, Ponnuthurai Nagaratnam, Zhang, Qingfu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.03.2011
Predmet:
Evolutionary multiobjective optimization
Multicriteria decision making
Multiobjective evolutionary algorithms
Multiobjective optimization
Multiobjective evolutionary algorithms
Multicriteria decision making
Multiobjective optimization
Evolutionary multiobjective optimization
ISSN:2210-6502
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.
ISSN:2210-6502
DOI:10.1016/j.swevo.2011.03.001