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A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High-Dimensional Optimization Problems

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Differential Evolution: From Theory to Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1009))

Abstract

Differential evolution (DE) algorithm is a core and widely used metaheuristic search algorithm since 1997. Notwithstanding, DE cannot produce qualified solutions for high-dimensional optimization problems. Artificial gorilla troops optimizer (AGTO) is a recently developed optimizer on continuous optimization problems and produced good solutions on high-dimensional optimization problems. Therefore, a new hybrid algorithm, artificial differential evolution gorilla troops optimizer (ADEGTO), is proposed for solving high-dimensional optimization problems. ADEGTO uses the explorative power of DE and the exploitative power of AGTO. DE has two peculiar parameters: F and CR. These peculiar parameters of the DE algorithm directly affect the solution quality. Generally, F and CR are determined intuitively or with limited or slipshod experiments. In this work, the first experiment on DE was conducted for determining the best F and CR parameters on high-dimensional optimization problems. A 100 dimensional 26 functions are used in experiments. F and CR parameters separately set as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9. Totally, 63,180 (9 × 9 × 26 × 30) experiments were done. In the second experiment, the optimal F and CR for ADEGTO are investigated. The third and fourth experiments contain a new variable F parameter investigation for ADEGTO and comparisons of ADEGTO with ten state-of-the-art algorithms, respectively.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Technology, Selçuk University, Konya, Turkey

    Ahmet Cevahir Cinar

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  1. Ahmet Cevahir Cinar
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Correspondence to Ahmet Cevahir Cinar .

Editor information

Editors and Affiliations

  1. Department of Information Technology, PSG College of Technology, Coimbatore, India

    B. Vinoth Kumar

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  3. Department of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    P. N. Suganthan

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Cinar, A.C. (2022). A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High-Dimensional Optimization Problems. In: Kumar, B.V., Oliva, D., Suganthan, P.N. (eds) Differential Evolution: From Theory to Practice. Studies in Computational Intelligence, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-16-8082-3_12

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