A new hybrid method DSM for parameter setting of meta-heuristic algorithms

Document Type : Industry Article

Authors

Khayyam University

Abstract

Parameters of meta-heuristic algorithms are very effective in their performance and are usually done experimentally, which is very time-consuming. In this research, a hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis methods and response surface methodology and is called DSM. In fact, this method can be used to optimize multi-objective problems and its main advantage is to create and optimize one performance response procedure instead of optimizing multiple output response procedures. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the proposed DSM method has been used to adjust the parameters of the cuckoo optimization algorithm to optimize the standard and experimental Aklay and Rastrigin functions. In the hybrid DSM method, first, the efficiency value is calculated using data envelopment analysis for each set of meta-heuristic algorithm parameters, then the response procedure for performance is determined according to the meta-heuristic algorithm parameters using the response surface methodology. Finally, by optimizing the efficiency surface, the optimal values of the cuckoo algorithm parameters are obtained. In order to validate, the results of the proposed method have been compared with a similar method. The results show better performance of the hybrid algorithm in terms of solution time, number of iterations, and accuracy of the optimization function compared to other similar methods.

Keywords

References

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Journal of Modeling in Engineering
Volume 19, Issue 65
June 2021
Pages 161-180

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History

  • Receive Date: 30 January 2021
  • Revise Date: 02 March 2021
  • Accept Date: 04 April 2021

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