Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

AbstractTo improve performances of multi-objective optimization algorithms, such as convergence and diversity, a hybridization- encouraged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm (NSGA-II). This mechanism uses the normalized 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