Constrained Multi-Objective Optimization Algorithm Based on Dual Populations

In order to improve the distribution and convergence of constrained multi-objective optimization algorithms, this paper proposes a constrained multi-objective optimization algorithm based on dual populations. The improved Harmonic distance eliminates the effect of the individuals whose Pareto grade is weak and distance is far, consequently the distribution of population can be enhanced. Also it reduces the amount of calculation effectively and improves the efficiency of the suggested algorithm. Then, the new update method of the infeasible solution set is closely linked with the feasible solution set, and these infeasible individuals both the objective function value and the constraint violation are excellent can be retained, so the better feasible individuals will be produced in the following evolution process, and the diversity of the populations and the search efficiency are improved simultaneously. Finally, the new variation strategy makes full use of the information of the best feasible individuals and the good infeasible individuals, which ensures the good ability of exploration and exploitation and balances the global and local search. The proposed algorithm is compared with 3 state-of-the-art constrained multi-objective optimization algorithms on CTP test problems. Simulation results show that the presented algorithm has certain advantages than other algorithms because it can ensure good convergence while it has uniform distribution.