Mo, HongweiXu, ZhidanXu, LifangWu, ZhouMa, Haiping2014-08-152014-08-152014-05-26Mo, H, Xu, Z, Xu, L, Wu, Z & Ma, H 2014, 'Constrained multiobjective biogeography optimization algorithm', The Scientific World Journal, vol. 2014, art. 232714, pp. 1-12.1537-744X10.1155/2014/232714http://hdl.handle.net/2263/41349Multiobjective 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.en© 2014 Hongwei Mo et al. This is an open access article distributed under the Creative Commons Attribution License.ConstraintsConstrained multiobjective biogeography optimization algorithm (CMBOA)Biogeography optimization algorithmConstrained multiobjective optimization problems (CMOPs)Evolutionary algorithms (EAs)Article