| 摘 要： | To improve performances of multi-objective optimization algorithms,such as convergence and diversity,a hybridization-encour-aged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm (NSGA-Ⅱ).This mechanism uses the nor-malized distance to evaluate the difference among genes in a population.Three possible modes of crossover operators-"Max Distance","Min-Max Distance",and "Neighboring-Max"-are suggested and analyzed.The mode of "Neighboring-Max",which not only takes advantage of hybridization but also improves the distribution of the population near Pareto optimal front,is chosen and used in NSGA-II on the basis of hybridization-encouraged mechanism (short for HEM-based NSGA-Ⅱ).To prove the HEM-based algorithm,several problems are studied by using standard NSGA-II and the presented method.Different evaluation criteria are also used to judge these algorithms in terms of distribution of solutions,convergence,diversity,and quality of solutions.The numerical results indicate that the application of hybridization-encouraged mechanism could effectively improve the performances of genetic algorithm.Finally,as an example in engineering practices,the presented method is used to design a longitudinal flight control system,which demonstrates the obtainability of a reasonable and correct Pareto front.
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