| Full text | |
| Author(s): |
Pereira, Joao Luiz Junho
;
Francisco, Matheus Brendon
;
de Almeida, Fabricio Alves
;
Ma, Benedict Jun
;
Cunha, Sebastiao Simoes
;
Gomes, Guilherme Ferreira
Total Authors: 6
|
| Document type: | Journal article |
| Source: | SOFT COMPUTING; v. N/A, p. 29-pg., 2023-07-06. |
| Abstract | |
Meta-heuristics have been successfully applied to many complex optimization problems. One of the main reasons for its success is its ability to handle non-convex, nonlinear, multimodal, multi-variable, and multi-objective problems with easy implementation. However, the quality of the response of these algorithms to an optimization problem is highly susceptible to the control parameters, and few works aim to tune them or find tools that can improve the algorithms. The literature is rich in proposals for new algorithms, but not for improving existing ones. This paper presents different strategies for tuning and accelerating meta-heuristics using the first hybrid algorithm in the literature. The Lichtenberg algorithm is inspired by lightning and Lichtenberg figures and has been increasingly successfully applied to various optimization problems. However, a study of its best parameters has never been presented until now. After a discussion of the best tuning tools, its tuning parameters are performed using response surface methodology. Then, 14 versions are studied through 10 test functions using chaos theory and Levy flights scenarios. After 13,500 simulations, the chaotic Lichtenberg algorithm equipped with the piecewise function and tuned parameters proved the best version with only 16% similarity to the original algorithm. Then, it was compared to the genetic algorithm, particle swarm optimization, gray wolf optimizer, salp swarm optimization, whale optimization algorithm, and dragonfly algorithm. The proposed algorithm had both the best average accuracy, lower computational cost, and the smallest standard deviation. (AU) | |
| FAPESP's process: | 22/10683-7 - Is my benchmark of datasets challenging enough? |
| Grantee: | João Luiz Junho Pereira |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |