TY - GEN
T1 - A model-based evolutionary algorithm for Bi-objective optimization
AU - Zhou, Aimin
AU - Zhang, Qingfu
AU - Jin, Yaochu
AU - Tsang, Edward
AU - Okabe, Tatsuya
PY - 2005
Y1 - 2005
N2 - The Pareto optimal solutions to a multi-objective optimization problem often distribute very regularly in both the decision space and the objective space. Most existing evolutionary algorithms do not explicitly take advantage of such a regularity. This paper proposed a model-based evolutionary algorithm (M-MOEA) for bi-objective optimization problems. Inspired by the ideas from estimation of distribution algorithms, M-MOEA uses a probability model to capture the regularity of the distribution of the Pareto optimal solutions. The Local Principal Component Analysis(Local PCA) and the least-squares method are employed for building the model. New solutions are sampled from the model thus built. At alternate generations, M-MOEA uses crossover and mutation to produce new solutions. The selection in M-MOEA is the same as in Non-dominated Sorting Genetic Algorithm-II(NSGA-II). Therefore, MOEA can be regarded as a combination of EDA and NSGA-II. The preliminary experimental results show that M-MOEA performs better than NSGA-II.
AB - The Pareto optimal solutions to a multi-objective optimization problem often distribute very regularly in both the decision space and the objective space. Most existing evolutionary algorithms do not explicitly take advantage of such a regularity. This paper proposed a model-based evolutionary algorithm (M-MOEA) for bi-objective optimization problems. Inspired by the ideas from estimation of distribution algorithms, M-MOEA uses a probability model to capture the regularity of the distribution of the Pareto optimal solutions. The Local Principal Component Analysis(Local PCA) and the least-squares method are employed for building the model. New solutions are sampled from the model thus built. At alternate generations, M-MOEA uses crossover and mutation to produce new solutions. The selection in M-MOEA is the same as in Non-dominated Sorting Genetic Algorithm-II(NSGA-II). Therefore, MOEA can be regarded as a combination of EDA and NSGA-II. The preliminary experimental results show that M-MOEA performs better than NSGA-II.
UR - https://www.scopus.com/pages/publications/27144492666
U2 - 10.1109/CEC.2005.1555016
DO - 10.1109/CEC.2005.1555016
M3 - 会议稿件
AN - SCOPUS:27144492666
SN - 0780393635
SN - 9780780393639
T3 - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
SP - 2568
EP - 2575
BT - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
PB - IEEE Computer Society
T2 - 2005 IEEE Congress on Evolutionary Computation, CEC 2005
Y2 - 2 September 2005 through 5 September 2005
ER -