TY - GEN
T1 - A novel multiobjective differential evolutionary algorithm based on subregion search
AU - Liu, Hai Lin
AU - Chen, Wen Qin
AU - Gu, Fangqing
N1 - This work was jointly supported by the Natural Science Foundation of China (60974077), the Natural Science Foundation of Guangdong Province (10251009001000002).
PY - 2012/6/10
Y1 - 2012/6/10
N2 - A novel multiobjective DE algorithm using the subregion and external set strategy (MOEA/S-DE) is proposed in this paper, in which the objective space is divided into some subregions and then independently optimize each subregion. An external set is introduced for each subregion to save some individuals ever found in this subregion. An alternative of mutation operators based the idea of direct simplex method of mathematical programming are proposed: local and global mutation operator. The local mutation operator is applied to improve the local search performance of the algorithm and the global mutation operator to explore a wider area. Additionally, a reusing strategy of difference vector also is proposed. It reuses the difference vector of the better individuals according to a given probability. Compared with traditional DE, the crossover operator also is improved. In order to demonstrate the performance of the proposed algorithm, it is compared with the MOEA/D-DE and the hybrid-NSGA-II-DE. The result indicates that the proposed algorithm is efficient.
AB - A novel multiobjective DE algorithm using the subregion and external set strategy (MOEA/S-DE) is proposed in this paper, in which the objective space is divided into some subregions and then independently optimize each subregion. An external set is introduced for each subregion to save some individuals ever found in this subregion. An alternative of mutation operators based the idea of direct simplex method of mathematical programming are proposed: local and global mutation operator. The local mutation operator is applied to improve the local search performance of the algorithm and the global mutation operator to explore a wider area. Additionally, a reusing strategy of difference vector also is proposed. It reuses the difference vector of the better individuals according to a given probability. Compared with traditional DE, the crossover operator also is improved. In order to demonstrate the performance of the proposed algorithm, it is compared with the MOEA/D-DE and the hybrid-NSGA-II-DE. The result indicates that the proposed algorithm is efficient.
UR - http://www.scopus.com/inward/record.url?scp=84866852706&partnerID=8YFLogxK
U2 - 10.1109/CEC.2012.6256153
DO - 10.1109/CEC.2012.6256153
M3 - Conference proceeding
AN - SCOPUS:84866852706
SN - 9781467315104
T3 - IEEE Congress on Evolutionary Computation, CEC
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
PB - IEEE
T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
Y2 - 10 June 2012 through 15 June 2012
ER -
