Fast Multi-objective Constrained Evolutionary Algorithm and Its Convergence

Aimed at the problems of slow pace of convergence and easy subsidence precocious problem, a new fast evolution algorithm is proposed for constrained multiobjective optimization problems. A crossover operator, which searches simultaneously from feasible and infeasible solution space is designed. Combining constraint condition and objective, a new partial-order relation for comparing individual is introduced. Thus, a new Niche computation method for maintaining diversity of population is suggested and repeat search is avoided using searched solution space. Based on all these, a novel effective evolution algorithm for global optimization is proposed and its convergence is proved. Compared with the current MOEAs, the simulation results show that this algorithm can rapidly converge at global Pareto solutions, and can maintain diversity of population.