TY - JOUR
T1 - Evolutionary many-objective algorithm using decomposition-based dominance relationship
AU - Chen, Lei
AU - Liu, Hai Lin
AU - Tan, Kay Chen
AU - CHEUNG, Yiu Ming
AU - Wang, Yuping
N1 - Funding Information:
Manuscript received January 10, 2018; revised March 21, 2018, June 5, 2018, and July 3, 2018; accepted July 15, 2018. Date of publication September 10, 2018; date of current version September 5, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61673121 and Grant 61672444, in part by the Projects of Science and Technology of Guangzhou under Grant 201804010352, in part by the City University of Hong Kong Research Fund under Grant 7200543, and in part by the China Scholarship Council. This paper was recommended by Associate Editor T. Ray. (Corresponding author: Hai-Lin Liu.) L. Chen and H.-L. Liu are with the Guangdong University of Technology, Guangzhou 510006, China (e-mail: [email protected]; [email protected]).
PY - 2019/12
Y1 - 2019/12
N2 - Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unscaled many-objective test problems verify the robust and generality of the proposed algorithm.
AB - Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unscaled many-objective test problems verify the robust and generality of the proposed algorithm.
KW - Dominance relationship
KW - Evolutionary algorithm
KW - Many-objective
KW - Multiobjective to multiobjective (M2M) decomposition
UR - http://www.scopus.com/inward/record.url?scp=85053109338&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2018.2859171
DO - 10.1109/TCYB.2018.2859171
M3 - Journal article
C2 - 30207973
AN - SCOPUS:85053109338
SN - 2168-2267
VL - 49
SP - 4129
EP - 4139
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 2
M1 - 8457246
ER -