Multi-objective and MGG evolutionary algorithm for constrained optimization

This paper presents a new approach to handle constrained optimization using evolutionary algorithms. The new technique converts constrained optimization to a two-objective optimization: one is the original objective function, the other is the degree function violating the constraints. By using Pareto-dominance in the multi-objective optimization, individual's Pareto strength is defined. Based on Pareto strength and minimal generation gap (MGG) model, a new real-coded genetic algorithm is designed. The new approach is compared with some other evolutionary optimization techniques on several benchmark functions. The results show that the new approach outperforms those existing techniques in feasibility, effectiveness and generality. Especially for some complicated optimization problems with inequality and equality constraints, the proposed method provides better numerical accuracy.