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Improved multi-objective genetic algorithm based on NSGA-II
基于NSGA-II的改进多目标遗传算法

Keywords: multi-objective optimization,genetic algorithm,crowing mechanism,crossover arithmetic operator,initial population
多目标优化
,遗传算法,排挤机制,交叉算子,初始种群

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Abstract:

Based on the study and analysis of NSGA-II algorithm, a new initial screening mechanism was designed, coefficient generating of crossover arithmetic operator was improved and more reasonable crowding mechanism was proposed. In this way, convergence was speeded up and its precision was improved. The testing results by representative applied functions show that with the improvements higher computational efficiency and more reasonable distributed solution can be obtained, and diversified distribution of the solutions can be maintained.

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