TY - GEN
T1 - Decomposition based dominance relationship for evolutionary many-objective algorithm
AU - Chen, Lei
AU - Liu, Hai Lin
AU - Tan, Kay Chen
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61673121, in part by the Projects of Science and Technology of Guangzhou under Grant 201508010008, in part by the PHD Start-up Fund of Natural Science Foundation of Guangdong Province (2014A030310257), and in part by the China Scholarship Council.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - Decomposition based evolutionary algorithms have achieved great success in solving many-objective optimization problems. However, the design of proper decomposition vectors is not an easy task, especially in high dimensional objective space. In this paper, we study how to better use these decomposition vectors. We first show that for any given decomposition vector, new dominance relationship and crowding measurement strategy can be well defined. Based on them, we then propose a new evolutionary algorithm for many objective optimization. By this way, the utilization efficiency of decomposition vectors is enhanced and thus the task of weights design is alleviated accordingly. Experiments are conducted to compare the proposed algorithm with four state-of-the-art decomposition based evolutionary algorithms on a set of well-known many-objective test problems with 5 to 10 objectives. The simulation results show that the proposed algorithm can achieve comparable results with fewer decomposition vectors.
AB - Decomposition based evolutionary algorithms have achieved great success in solving many-objective optimization problems. However, the design of proper decomposition vectors is not an easy task, especially in high dimensional objective space. In this paper, we study how to better use these decomposition vectors. We first show that for any given decomposition vector, new dominance relationship and crowding measurement strategy can be well defined. Based on them, we then propose a new evolutionary algorithm for many objective optimization. By this way, the utilization efficiency of decomposition vectors is enhanced and thus the task of weights design is alleviated accordingly. Experiments are conducted to compare the proposed algorithm with four state-of-the-art decomposition based evolutionary algorithms on a set of well-known many-objective test problems with 5 to 10 objectives. The simulation results show that the proposed algorithm can achieve comparable results with fewer decomposition vectors.
KW - decomposition
KW - dominance relationship
KW - evolutionary algorithm
KW - Many-objective
UR - http://www.scopus.com/inward/record.url?scp=85046017452&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8280867
DO - 10.1109/SSCI.2017.8280867
M3 - Conference article published in proceeding or book
AN - SCOPUS:85046017452
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 6
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -