An Intelligence Model with Max-Min Strategy for Constrained Evolutionary Optimization

摘要

An intelligence model (IM) is proposed for constrained optimization in this paper. In this model, two main issues are considered: first, handling feasible and infeasible individuals in population, and second, recognizing the piecewise continuous Pareto front to avoid unnecessary search, it could reduce the amount of calculation and improve the efficiency of search. In addition, max-min strategy is used in selecting optimization. By integrating IM with evolutionary algorithm (EA), a generic constrained optimization evolutionary (IMEA) is derived. The new algorithm is applied to tackle 7 test instances on the CEC2009 MOEA competition, and the performance is assessed by IGD metric, the results suggest that it outperforms or performs similarly to other algorithms in CEC2009 competition.