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

Zhou, Aimin and Qu, Bo-Yang and Li, Hui and Zhao, Shi-Zheng and Suganthan, Ponnuthurai Nagaratnam and Zhang, Qingfu (2011) 'Multiobjective evolutionary algorithms: A survey of the state of the art.' Swarm and Evolutionary Computation, 1 (1). pp. 32-49. ISSN 2210-6502

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Abstract

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. © 2011 Elsevier B.V. All rights reserved.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Jan 2012 11:28
Last Modified: 15 Jan 2022 00:57
URI: http://repository.essex.ac.uk/id/eprint/1968

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