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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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Published in:Scientific World Journal pp. 232714 - 232715
Main Authors: Mo, Hongwei, Xu, Zhidan, Xu, Lifang, Wu, Zhou, Ma, Haiping
Format: research Journal Article
Published: Wiley-Blackwell 2014
Online Access:View this record in EBSCOhost
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      jtl: Scientific World Journal
      issn: 1537744X
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    pubinfo:
      dt: 2014
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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      ui:
        103833002
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        2012642987
        10.1155/2014/232714
        NLM25006591
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        103833002
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        atl: Constrained multiobjective biogeography optimization algorithm.
      aug:
        au:
          Mo, Hongwei
          Xu, Zhidan
          Xu, Lifang
          Wu, Zhou
          Ma, Haiping
        affil: Automation College, Harbin Engineering University, Harbin 150001, China.
      sug:
        subj:
          Algorithms
          Evolution
          Genetics
          Geographic Locations
          Population
          Animal Studies
          Animals
          Human
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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