Sharing mutation genetic algorithm for solving multi-objective problems
Sheng-Ta Hsieh, Shih-Yuan Chiu, Shi-Jim Yen · 2011
Multi-objective optimization (MO) has been an active area of research in last two decade. In multi-objective genetic algorithm (MOGA), quality of new generated offspring of population will affect the performance of finding Pareto optimum directly. In this paper, an improved MOGA is proposed named SMGA to solving multi-objective optimization problem. For improving solution searching efficiency, an effective mutation named sharing mutation is adopted for generating potential offspring. Experiments were conducted on CEC-09 MOP test problems. The results showed that the proposed method exhibits better performance when solving these benchmark problems compared to related multi-objective evolutionary algorithm (MOEA).