Title :
A novel multiobjective differential evolutionary algorithm based on subregion search
Author :
Liu, Hai-Lin ; Chen, Wen-qin ; Gu, Fangqing
Author_Institution :
Fac. of Appl. Math., Guangdong Univ. of Technol., Guangzhou, China
Abstract :
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.
Keywords :
evolutionary computation; mathematical programming; set theory; crossover operator; direct simplex method; external set strategy; global mutation operator; hybrid-NSGA-II-DE; local mutation operator; mathematical programming; multiobjective differential evolutionary algorithm; reusing strategy; subregion search; subregion strategy; Algorithm design and analysis; Educational institutions; Evolutionary computation; Optimization; Standards; Vectors;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6256153