TY - GEN
T1 - Evolutionary Dynamic Multi-objective Optimization via Regression Transfer Learning
AU - Wang, Zhenzhong
AU - Jiang, Min
AU - Gao, Xing
AU - Feng, Liang
AU - Hu, Weizhen
AU - Tan, Kay Chen
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No.61673328) and Shenzhen Scientific Research and Development Funding Program (Grant No. JCYJ20180307123637294).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values. Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objective optimization algorithms and is competitive in convergence and diversity.
AB - Dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values. Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objective optimization algorithms and is competitive in convergence and diversity.
KW - dynamic multi-objective optimization
KW - evolutionary algorithm
KW - regression prediction
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85080866444&partnerID=8YFLogxK
U2 - 10.1109/SSCI44817.2019.9002942
DO - 10.1109/SSCI44817.2019.9002942
M3 - Conference article published in proceeding or book
AN - SCOPUS:85080866444
T3 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
SP - 2375
EP - 2381
BT - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Y2 - 6 December 2019 through 9 December 2019
ER -