Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (2): 319-326.doi: 10.21629/JSEE.2019.02.11

• Systems Engineering • Previous Articles     Next Articles

Evolutionary many objective optimization based on bidirectional decomposition

Chengzhong LYU*(), Weimin LI()   

  • Received:2018-05-16 Online:2019-04-01 Published:2019-04-28
  • Contact: Chengzhong LYU;liweimin
  • About author:LYU Chengzhong was born in 1990. He received his M.S. degree from Air Force Engineering University (AFEU). He is currently pursuing his Ph.D. degree at AFEU. His research interests include operatonal deployment, operational assessment and decision-making optimization.|LI Weimin was born in 1964. He received his doctorate from the University of Electronic Science and Technology. His research interests include aerospace defense system construction, command and control planning, natural computation and intelligent information processing. E-mail:liweimin


The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.

Key words: many objective optimization, bidirectional decomposition, reference update, evolutionary algorithm