Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems

Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisa...

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Bibliographic Details
Published in:	Swarm and evolutionary computation Vol. 14; pp. 31 - 47
Main Authors:	Helbig, Mardé, Engelbrecht, Andries P.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2014
Subjects:	Dynamic multi-objective optimization Dynamic multi-objective optimization algorithms Population-based algorithms Dynamic multi-objective optimization Population-based algorithms Dynamic multi-objective optimization algorithms
ISSN:	2210-6502
Online Access:	Get full text
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Summary:	Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisation (MOO) have focussed on static MOO (SMOO) and dynamic single-objective optimisation. However, in recent years, algorithms were proposed to solve dynamic MOO (DMOO). This paper provides an overview of the algorithms that were proposed in the literature to solve DMOOPs. In addition, challenges, practical aspects and possible future research directions of DMOO are discussed.
ISSN:	2210-6502
DOI:	10.1016/j.swevo.2013.08.004