Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

Yong Wang, Jian Yu, Shengxiang Yang* (Corresponding Author), Shouyong Jiang, Shuang Zhao* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions.

Original languageEnglish
Article number100559
Number of pages23
JournalSwarm and Evolutionary Computation
Early online date30 Jul 2019
Publication statusPublished - Nov 2019

Bibliographical note

Funding Information:
This work was supported in part by the Innovation-Driven Project of Central South University under Grant 2018CX010 , in part by the National Natural Science Foundation of China under Grant 61673397 , in part by the Hunan Provincial Natural Science Fund for Distinguished Young Scholars (Grant No. 2016JJ1018 ), and in part by the Beijing Advanced Innovation Center for Intelligent Robots and Systems under Grant 2018IRS06 .

Publisher Copyright:
© 2019 Elsevier B.V.


  • Benchmark test functions
  • Constraint-handling technique
  • Dynamic constrained optimization
  • Evolutionary algorithms
  • Performance comparison


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