A Modified Slime Mould Algorithm for Global Optimization.
Comput Intell Neurosci
; 2021: 2298215, 2021.
Article
en En
| MEDLINE
| ID: mdl-34912443
ABSTRACT
Slime mould algorithm (SMA) is a population-based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition-based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed-dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real-world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Comput Intell Neurosci
Asunto de la revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
China