A game model based co-evolutionary for constrained multiobjective optimization problems

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as multiobjective optimization problems (MOPs)) has attracted much attention recently. Population based approaches, such as EAs, offer a means to find a group of Pareto-optimal solutions in a single run. However, most studies are undertaken on unconstrained MOPs. Recently, we developed the co-evolutionary algorithms for unconstrained MOPs. The objective of this paper is to introduce a modification to co-evolutionary algorithms for handling constraints. The solutions, provided by the proposed algorithm for one test problem, are promising when compared with an existing well-known algorithm.