Constrained Multiobjective Biogeography Optimization Algorithm

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained m...

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Vydané v:TheScientificWorld Ročník 2014; číslo 2014; s. 1 - 12
Hlavní autori: Ma, Hongping, Wu, Zhou, Xu, Lifang, Xu, Zhidan, Mo, Hongwei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Wiley
Predmet:
Algorithms
Animals
Biogeography
Biological Evolution
Colleges & universities
Computer engineering
Genetic algorithms
Humans
Mathematical optimization
Mathematical research
Migration
Nonlinear programming
Operator theory
Pareto optimum
Phylogeography - methods
Population
ISSN:2356-6140, 1537-744X, 1537-744X
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Shrnutí:Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editors: V. Bhatnagar and Y. Zhang
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/232714