Sharing mutation genetic algorithm for solving multi-objective problems
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 SM...
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| creator | Sheng-Ta Hsieh Shih-Yuan Chiu Shi-Jim Yen |
| description | 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). |
| doi_str_mv | 10.1109/CEC.2011.5949838 |
| format | Conference Proceeding |
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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).</abstract><pub>IEEE</pub><doi>10.1109/CEC.2011.5949838</doi><tpages>7</tpages></addata></record> |
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| ispartof | 2011 IEEE Congress of Evolutionary Computation (CEC), 2011, p.1833-1839 |
| issn | 1089-778X 1941-0026 |
| language | eng |
| recordid | cdi_ieee_primary_5949838 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological cells Convergence Evolutionary computation genetic algorithm Genetic algorithms multi-objective Optimization Search problems sharing mutation Space exploration |
| title | Sharing mutation genetic algorithm for solving multi-objective problems |
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