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Improved multi-objective genetic algorithm based on NSGA-II

Keywords: multi-objective optimization,genetic algorithm,crowing mechanism,crossover arithmetic operator,initial population

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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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