Title :
Sharing mutation genetic algorithm for solving multi-objective problems
Author :
Hsieh, Sheng-Ta ; Chiu, Shih-Yuan ; Yen, Shi-Jim
Author_Institution :
Dept. of Commun. Eng., Oriental Inst. of Technol., Taipei, Taiwan
Abstract :
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).
Keywords :
Pareto optimisation; genetic algorithms; CEC-09 MOP test problems; Pareto optimum; multiobjective evolutionary algorithm; multiobjective genetic algorithm; multiobjective problems; mutation genetic algorithm; sharing mutation; Biological cells; Convergence; Evolutionary computation; Genetic algorithms; Optimization; Search problems; Space exploration; genetic algorithm; multi-objective; optimization; sharing mutation;
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-7834-7
DOI :
10.1109/CEC.2011.5949838