The effect of different stopping criteria on multi-objective optimization algorithms

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Issue Date
2021-02-20
Authors
El-Abd, Mohammed
Abou Doush, Iyad
Bataineh, Mohammad
Hammouri, Abdelaziz
Keywords
Type
Journal Article
Peer-Reviewed
Abstract
Evolutionary multi-objective optimization (EMO) refers to the domain in which an evolutionary algorithm is applied to tackle an optimization problem with multiple objective functions. The literature is rich with many approaches proposed to solve multi-objective problems including the NSGA-II, MOEA/D, and MOPSO algorithms. The proposed approaches include stand-alone as well as hybrid techniques. One critical aspect of any evolutionary algorithm (EA) is the stopping criterion. The selection of a specific stopping criterion can have a considerable effect on the performance and the final solution provided by the EA. A number of different stopping criteria, specifically designed for EMO, have been proposed in the literature. In this paper, the performance of six different EMO algorithms is tested and compared using four stopping criteria. The experiments are performed using the ZDT, DTLZ, CEC2009, Tanaka and Srivana test functions. Experimental results are analyzed to highlight the proper stopping criteria for different algorithms.
Citation
Abu Doush, I., El-Abd, M., Hammouri, A. I., & Bataineh, M. Q. (2021). The effect of different stopping criteria on multi-objective optimization algorithms. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-05805-1