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Multi-Objective Evolutionary Algorithms

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

Evolutionary algorithms (GlossaryTerm

EA

s) have amply shown their promise in solving various search and optimization problems for the past three decades. One of the hallmarks and niches of GlossaryTerm

EA

s is their ability to handle multi-objective optimization problems in their totality, which their classical counterparts lack. Suggested in the beginning of the 1990s, evolutionary multi-objective optimization (GlossaryTerm

EMO

) algorithms are now routinely used in solving problems with multiple conflicting objectives in various branches of engineering, science, and commerce. In this chapter, we provide an overview of GlossaryTerm

EMO

methodologies by first presenting principles of GlossaryTerm

EMO

through an illustration of one specific algorithm and its application to an interesting real-world bi-objective optimization problem. Thereafter, we provide a list of recent research and application developments of GlossaryTerm

EMO

to provide a picture of some salient advancements in GlossaryTerm

EMO

research. The development and application of GlossaryTerm

EMO

to multi-objective optimization problems and their continued extensions to solve other related problems has elevated GlossaryTerm

EMO

research to a level which may now undoubtedly be termed as an active field of research with a wide range of theoretical and practical research and application opportunities.

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Abbreviations

ARMOGA:

adaptive range MOGA

EA:

evolutionary algorithm

EMO:

evolutionary multiobjective optimization

FEMO:

fair evolutionary multi-objective optimizer

GA:

genetic algorithm

IBEA:

indicator-based evolutionary algorithm

KKT:

Karush–Kuhn–Tucker

KUR:

Kurswae

LOTZ:

leading ones trailing zeroes

MCDA:

multi-criteria decision analysis

MCDM:

multiple criteria decision-making

MOGA:

multiobjective genetic algorithm

MOMGA:

multi-objective messy GA

NSGA:

nondominated sorting genetic algorithm

PAES:

Pareto-archived evolution strategy

PCA:

principal component analysis

PESA:

Pareto-envelope based selection algorithm

SBX:

simulated binary crossover

SEMO:

simple evolutionary multi-objective optimizer

SPEA:

strength Pareto evolutionary algorithm

VEGA:

vector-evaluated GA

ZDT:

Zitzler–Deb–Thiele

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  1. Dep. Electrical and Computer Engineering, Michigan State University, 428 S. Shaw Lane, MI 48824, East Lansing, USA

    Kalyanmoy Deb

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  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Deb, K. (2015). Multi-Objective Evolutionary Algorithms. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_49

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