A Decomposition-Based Constraint-Handling Technique for Constrained Multi-objective Optimization

摘要

In recent years, many constraint-handling techniques maintaining infeasible individuals in the population have demonstrated their effectiveness for solving constrained optimization problems. Generally, the effective utilization of the potential of the infeasible individuals will improve the performance of the algorithms. This paper proposes an approach based on decomposition for constrained multi-objective optimization problems. By transforming the degree of constraint violation into one objective, a constraint-handling technique is introduced to exploit useful infeasible individuals during the evolution process. Besides, along with the evolution, it puts more emphasis on infeasible individuals with better objective values and smaller constraint violations, since they can provide more importance information for improving the convergence and diversity of the population. The performance of the proposed algorithm is evaluated by using 8 well-known test instances, i.e. CTP-series test instances. And the experimental results indicate that our algorithm is superior to the compared algorithm.