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Parallel Multiobjective Evolutionary Algorithms

  • Chapter
Springer Handbook of Computational Intelligence

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

Abstract

The use of evolutionary algorithms (GlossaryTerm

EA

s) for solving multiobjective optimization problems has been very active in the last few years. The main reasons for this popularity are their ease of use with respect to classical mathematical programming techniques, their scalability, and their suitability for finding trade-off solutions in a single run. However, these algorithms may be computationally expensive because (1) many real-world optimization problems typically involve tasks demanding high computational resources and (2) they are aimed at finding a whole front of optimal solutions instead of searching for a single optimum. Parallelizing GlossaryTerm

EA

s emerges as a possible way of reducing the GlossaryTerm

CPU

time down to affordable values, but it also allows researchers to use an advanced search engine – the parallel model – that provides the algorithms with an improved population diversity and enable them to cooperate with other (eventually nonevolutionary) techniques. The goal of this chapter is to provide the reader with an up-to-date review of the recent literature on parallel GlossaryTerm

EA

s for multiobjective optimization.

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Abbreviations

A2A:

all-to-all

CEA:

cellular evolutionary algorithm

cGA:

compact genetic algorithm

cMOEA:

cellular MOEA

CPF:

centralized Pareto front

CPU:

central processing unit

CUDA:

compute unified device architecture

dEA:

distributed evolutionary algorithm

DGA:

direct genetic algorithm

dMOEA:

distributed MOEA

DPF:

distributed Pareto front

DTLZ:

Deb–Thiele–Laumanns–Zitzler

EA:

evolutionary algorithm

EMO:

evolutionary multiobjective optimization

FA-DP:

fitness assignment and diversity preservation

FDA:

factorized distribution algorithm

GPU:

graphics processing unit

IBEA:

indicator-based evolutionary algorithm

IB:

indicator based

MOEA:

multiobjective evolutionary algorithm

MOGA:

multiobjective genetic algorithm

MOP:

multiobjective optimization problem

MPI:

message passing interface

MS:

master/slave

msMOEA:

master–slave MOEA

NSGA:

nondominated sorting genetic algorithm

PAES:

Pareto-archived evolution strategy

PFC:

Pareto front computation

PM:

parallel model

PP:

parallel platform

SRF:

strength raw fitness

WFG:

walking fish group

ZDT:

Zitzler–Deb–Thiele

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Authors and Affiliations

  1. Centro Universitario de Mérida, Santa Teresa de Jornet 38, 06800, Mérida, Spain

    Francisco Luna

  2. E.T.S.I. Informática, Universidad de Malaga, Campus de Teatinos (3.2.12), 29071, Málaga, Spain

    Enrique Alba

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  1. Francisco Luna
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  2. Enrique Alba
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Correspondence to Francisco Luna .

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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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Luna, F., Alba, E. (2015). Parallel Multiobjective 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_50

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_50

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