An evolutionary algorithm for constrained multi-objective optimization problems

Constrained multi-objective optimization problems (CMOP) are challenging and difficult to solve. In this paper, a simple and practical evolutionary algorithm for constrained multi-objective optimization problems (EA CMOP) is presented, by defining constraints using non-parameter punitive functions, using Pareto strength value to represent Pareto order strength among individuals and using crowding density to ensure group diversity. It defines the evolutionary algorithm fitness functions by combining constraint treatment, comparison of Pareto strength optimization and crowding density. Test results on several benchmark functions showed that the approach is effective and robust.