Constraint handling in multi-objective evolutionary optimization

This paper introduces a new constraint handling technique for multi-objective evolutionary algorithms based on adaptive penalty functions and distance measures of an individual. These two values are used to modify the objective space. The modified objective functions are used in the non- dominance sorting so that the algorithm evolves feasible optimal solutions not only from the feasible space but also from the infeasible space. The search in the infeasible space is designed to encourage those individuals with better objective value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible solutions or toward finding optimum solutions. The proposed method is simple to implement and does not need any parameter tuning. The constraint handling technique was tested on several constrained multi-objective problems and has shown superior results.