Blucher Physics Proceedings

Março 2021, vol.7, num.1 - XI Encontro Científico de Física Aplicada
Artigo completo - Open Access Idioma principal | Segundo principal

A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function

A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function

Dalla, C. E. R; da Silva, W. B.; Dutra, J. C. S.; Colaço, J. M.

Artigo completo:

Optimization methods are frequently applied to solve real-world problems such, engineering design, computer science, and computational chemistry. This paper aims to compare gradient-based algorithms and the meta-heuristic particle swarm optimization to minimize the multidimensional benchmark Griewank function, a multimodal function with widespread local minima. Several approaches of gradient-based methods such as steepest descent, conjugate gradient with Fletcher-Reeves and Polak- Ribiere formulations, and quasi-Newton Davidon-Fletcher-Powell approach were compared. The results presented showed that the meta-heuristic method is recommended for function with this behavior because is no needed prior information of the search space. The performance comparison includes computation time and convergence of global and local optimum.

Optimization methods are frequently applied to solve real-world problems such, engineering design, computer science, and computational chemistry. This paper aims to compare gradient-based algorithms and the meta-heuristic particle swarm optimization to minimize the multidimensional benchmark Griewank function, a multimodal function with widespread local minima. Several approaches of gradient-based methods such as steepest descent, conjugate gradient with Fletcher-Reeves and Polak- Ribiere formulations, and quasi-Newton Davidon-Fletcher-Powell approach were compared. The results presented showed that the meta-heuristic method is recommended for function with this behavior because is no needed prior information of the search space. The performance comparison includes computation time and convergence of global and local optimum.

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DOI: 10.5151/xiecfa-Dalla

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Como citar:

Dalla, C. E. R; da Silva, W. B.; Dutra, J. C. S.; Colaço, J. M.; "A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function", p-156-160. In: Anais do XI Encontro Científico de Física Aplicada. São Paulo: Blucher, 2021.
ISSN 23582359, DOI 10.5151/xiecfa-Dalla

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