Adaptively Enhancing Infeasible Region Exploration on Multiple Constraint Ranking

Abstract

In the present work, we propose some modifications on multiple constraint ranking (MCR) to improve its performance in handling constraints in engineering design optimization with evolutionary algorithm. Considering that the search from both feasible and infeasible regions is more efficient, the modifications are proposed so that MCR can adaptively conduct more exploration in infeasible region. Based on investigation in a car structure design optimization problem, some of the proposed modifications have proven to be significantly effective in enhancing the convergence performance towards constrained optimum in this particular problem compared with the original MCR. We discover that the modifications which significantly improve the convergence performance produce more variety of infeasible individuals compared with MCR. Also, many of infeasible individuals produced by those modifications have better objective values than the feasible ones while we hardly observe similar occurrence in MCR. These two factors might make those modifications have better interaction between feasible and infeasible regions, thus produce convergence improvement on MCR.