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Statistical Measurements of Metaheuristics for Solving Engineering Problems

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Recent Advances in Computational Optimization (WCO 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 986))

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Abstract

Comparing the results obtained by two or more algorithms in a set of problems is a central task in optimizing or machine learning. Drawing conclusions from these comparisons may require the use of statistical tools such as hypothesis testing. In this paper, we investigate the utilization of parametric multi compared statistical tests on our proposed approach’s performance and the rest of the metaheuristics for solving engineering problems. Our proposed strategy (BSGM) includes the Bat algorithm, Simulated annealing, Gaussian distribution, and a novel mutation operator. The proposed method balances the Bat algorithm’s critical exploitation and global exploration of the Simulated annealing. The literature’s common engineering problems were analyzed in the competition between our BSGM approach and the latest swarm intelligence algorithms. We showed that all algorithms do not produce the same performance using multi compared analysis of variance (ANOVA). The benchmark results also show that our BSGM method provides encouraging results and can compare with the latest metaheuristics according to high-quality solutions and a small number of function evaluations.

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Authors and Affiliations

  1. Department of Mathematics, University of Sarajevo, Zmaja od Bosne 33-35, 71000, Sarajevo, Bosnia and Herzegovina

    Adis Alihodzic

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  1. Adis Alihodzic
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Correspondence to Adis Alihodzic .

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  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Alihodzic, A. (2022). Statistical Measurements of Metaheuristics for Solving Engineering Problems. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2020. Studies in Computational Intelligence, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-030-82397-9_1

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