Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (1): 132-143.doi: 10.21629/JSEE.2019.01.13

• Systems Engineering • Previous Articles     Next Articles

Double adaptive selection strategy for MOEA/D

Jiale GAO*(), Qinghua XING(), Chengli FAN(), Zhibing LIANG()   

  • Received:2017-07-06 Online:2019-02-27 Published:2019-02-27
  • Contact: Jiale GAO E-mail:gaojiale_kgd@163.com;liuxqh@126.com;ff516@163.com;liangzhibing@163.com
  • About author:GAO Jiale was born in 1990. He received his M.S. degree from Air Force Engineering University (AFEU) in 2015. He is currently pursuing his Ph.D. degree at AFEU. His research interests include the evolutionary multi-objective optimization, and sensor resource scheduling. E-mail:gaojiale_kgd@163.com|XING Qinghua was born in 1966. She is a professor, and received her Ph.D. degree from Air Force Engineering University (AFEU) in 2003. Her research interests include systems modeling and simulation, operation decision analysis in air and missile defense. E-mail:liuxqh@126.com|FAN Chengli was born in 1988. She is a Ph.D. and a lecturer in Air force Engineering University. Her research interests include intelligent optimization algorithm, intelligent information processing, and military battle modeling & simulation. E-mail:ff516@163.com|LIANG Zhibing was born in 1990. He received his M.S. degree from Air Force Engineering University (AFEU) in 2015. He is presently working towards his Ph.D. degree at AFEU. His main research interests include multi-target tracking, and information fusion. E-mail:lzb liangzhibing@163.com
  • Supported by:
    the National Natural Science Foundation of China(71771216);the National Natural Science Foundation of China(71701209);This work was supported by the National Natural Science Foundation of China (71771216; 71701209)

Abstract:

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems. In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection (DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS (MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.

Key words: multi-objective optimization, adaptive operator selection, adaptive neighbor selection, decomposition