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.