Multi-objective Optimization based on a Multiple-crossover Genetic Algorithm

In this paper, first a multiple-crossover genetic algorithm is presented. Its operators such as reproduction, crossover and mutation are introducedcompletely. Some multi-objective benchmark problems are selected to challenge the ability of the proposed method. Optimization is based on the non-dominated sorting idea. Simulation results are presented. The results are compared with true Pareto-optimal solutions to evaluate the performance of the proposed method

کلیدواژه ها:

genetic algorithm ، multiple-crossover ، multi-objective optimization ، Pareto-optimal