title: Constrained multiobjective biogeography optimization algorithm.
abstract: 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.
authors.affiliation: Automation College, Harbin Engineering University, Harbin 150001, China.
authors.forename: Hongwei
authors.initials: H
authors.lastname: Mo
authors.affiliation: Institute of Basic Science, Harbin University of Commerce, Harbin 150028, China.
authors.forename: Zhidan
authors.initials: Z
authors.lastname: Xu
authors.affiliation: Engineering Training Center, Harbin Engineering University, Harbin 150001, China.
authors.forename: Lifang
authors.initials: L
authors.lastname: Xu
authors.affiliation: Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Gauteng 0028, South Africa.
authors.forename: Zhou
authors.initials: Z
authors.lastname: Wu
authors.affiliation: Department of Electrical Engineering, Shaoxing University, Shaoxing, Zhejiang 312000, China.
authors.forename: Haiping
authors.initials: H
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D006801:Humans; D058974:Phylogeography / Q000379:methods*