Applied Mathematics & Information Sciences

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Multi-objective optimization (MO) has been an active area of research in the last two decades. In multi-objective genetic algorithm (MOGA), the quality of newly generated offspring of the population will directly affect the performance of finding the Pareto optimum. In this paper, an improved MOGA, named SMGA, is proposed for solving multi-objective optimization problems. To increase efficiency during solution searching, an effective mutation named sharing mutation is adopted to generate potential offspring. Experiments were conducted on CEC-09 MOP test problems. The results show that the proposed method exhibits better performance when solving these benchmark problems compared to related multi-objective evolutionary algorithms (MOEA).

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