Improved NSGA-Ⅱ Multi-objective Gene

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

To improve performances of muhi-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 bybridization-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.

作 者： Sun Yijie Shen Gongzhang   作者单位： School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China  刊 名： 中国航空学报（英文版）  ISTIC 英文刊名： CHINESE JOURNAL OF AERONAUTICS  年，卷(期)： 2008 21(6)  分类号： V2  关键词： multi-objective optimization   genetic algorithms   diversity   hybridization   crossover  

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