Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

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...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 50; S. 100559
Hauptverfasser: Wang, Yong, Yu, Jian, Yang, Shengxiang, Jiang, Shouyong, Zhao, Shuang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.11.2019
Schlagworte:
Benchmark test functions
Constraint-handling technique
Dynamic constrained optimization
Evolutionary algorithms
Performance comparison
Performance comparison
Evolutionary algorithms
Constraint-handling technique
Dynamic constrained optimization
Benchmark test functions
ISSN:2210-6502
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Zusammenfassung: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.
ISSN:2210-6502
DOI:10.1016/j.swevo.2019.100559