TY - GEN
T1 - Two-stage assortative mating for multi-objective multifactorial evolutionary optimization
AU - Yang, Cuie
AU - Ding, Jinliang
AU - Tan, Kay Chen
AU - Jin, Yaochu
N1 - Funding Information:
* Research supported by NSFC Projects under Grants 61525302 and 61590922, the Projects of Liaoning Province under Grant 2014020021 and LR2015021, N160801001 and N161608001 and Y17-0-004.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/18
Y1 - 2018/1/18
N2 - A multi-objective multifactorial evolutionary algorithm has been proposed recently to address multi-objective multi-tasks optimization simultaneously. However, the approach only focuses on the improvement of algorithm convergence via knowledge transfer among the optimization tasks. To enhance the performance of both diversity and convergence which are important for evolutionary multi-objective optimization, this paper proposes a two-stage assortative mating method for multi-objective multifactorial evolutionary optimization. In the proposed algorithm, decision variables are first divided into two types using a decision variable clustering method: Diversity-related variables and convergence-related variables. The two types of variables then undergo assortative mating with different parameters independently when offspring are generated. Experimental results on a variety of test instances show that the proposed algorithm is highly competitive as compared with existing multi-task and single-task algorithms.
AB - A multi-objective multifactorial evolutionary algorithm has been proposed recently to address multi-objective multi-tasks optimization simultaneously. However, the approach only focuses on the improvement of algorithm convergence via knowledge transfer among the optimization tasks. To enhance the performance of both diversity and convergence which are important for evolutionary multi-objective optimization, this paper proposes a two-stage assortative mating method for multi-objective multifactorial evolutionary optimization. In the proposed algorithm, decision variables are first divided into two types using a decision variable clustering method: Diversity-related variables and convergence-related variables. The two types of variables then undergo assortative mating with different parameters independently when offspring are generated. Experimental results on a variety of test instances show that the proposed algorithm is highly competitive as compared with existing multi-task and single-task algorithms.
UR - https://www.scopus.com/pages/publications/85046140744
U2 - 10.1109/CDC.2017.8263646
DO - 10.1109/CDC.2017.8263646
M3 - Conference article published in proceeding or book
AN - SCOPUS:85046140744
T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
SP - 76
EP - 81
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -