Journal
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
Volume 33, Issue 1, Pages 109-125Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0952813X.2020.1725650
Keywords
Learning automata; gravitational search algorithm; unconstrained optimisation
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The gravitational search algorithm based on learning automata (GSA-LA) is more efficient in finding optimum solutions compared to the original GSA and other evolutionary-based algorithms, by utilizing learning capability to select parameters based on spontaneous reactions.
Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. Gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms.
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