A modified objective function method with feasible-guiding strategy to solve constrained 
multi-objective optimization problems

Abstract

For constrained multi-objective optimization problems (CMOPs), how to preserve infeasible individuals 
and make use of them is a problem to be solved. In this case, a modified objective function method with 
feasible-guiding strategy on the basis of NSGA-II is proposed to handle CMOPs in this paper. The main 
idea of proposed algorithm is to modify the objective function values of an individual with its constraint 
violation values and true objective function values, of which a feasibility ratio fed back from current 
population is used to keep the balance, and then the feasible-guiding strategy is adopted to make use of 
preserved infeasible individuals. In this way, non-dominated solutions, obtained from proposed algorithm, 
show superiority on convergence and diversity of distribution, which can be confirmed by the comparison 
experiment results with other two CMOEAs on commonly used constrained test problems.