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Statistical Investigation of the Robustness for the Optimization Algorithms

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Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Optimization algorithms are widely used in engineering applications to solve complex real-world design problems. The success of an algorithm with a vast scope should be a convergence in a short time with less iteration to keep pace with developing technology. Generally, optimization algorithms are performed without examining the effect of the algorithm parameters and characteristic of the optimization problem, statistically. Therefore, the appropriate algorithm parameter values of the scatter search optimization algorithms in this chapter have been aimed to investigate with the Taguchi method, which is a statistics-based and the robust way. By manipulating fewer trials, it is possible to gain the proper value, which changes according to the minimum or the maximum global solution design among all combinations of the algorithm parameters. Utilizing an orthogonal array proposed by Taguchi is an effective and successful tool. Thus, approaches are intended to achieve the results with less repetition and to increase the accomplishment of the algorithm, taking into account the effect of the algorithm parameters on the result with statistical analysis.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, KTO Karatay University, Konya, Turkey

    Esra Uray

  2. Department of Computer Engineering, Necmettin Erbakan University, Konya, Turkey

    Huseyin Hakli

  3. Department of Civil Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Serdar Carbas

Authors
  1. Esra Uray
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  2. Huseyin Hakli
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  3. Serdar Carbas
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Corresponding author

Correspondence to Esra Uray .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Serdar Carbas

  2. Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey

    Abdurrahim Toktas

  3. Department of Computer Engineering, Tarsus University, Tarsus, Turkey

    Deniz Ustun

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Uray, E., Hakli, H., Carbas, S. (2021). Statistical Investigation of the Robustness for the Optimization Algorithms. In: Carbas, S., Toktas, A., Ustun, D. (eds) Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-33-6773-9_10

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