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Technical gazette, Vol. 28 No. 2, 2021.

Original scientific paper

https://doi.org/10.17559/TV-20191217030336

Parallel Acceleration and Improvement of Gravitational Field Optimization Algorithm

Lan Huang ; Jilin University, College of Computer Science and Technology, Changchun, CN 130012
Wen-Xuan Wu ; Jilin University, College of Computer Science and Technology, Changchun, CN 130012
Xue-Mei Hu* ; Jilin University, College of Computer Science and Technology, Changchun, CN 130012
Sen Yang ; Jilin University, College of Computer Science and Technology, Changchun, CN 130012
Yu Lin ; Jilin University, College of Artificial Intelligence, Changchun, CN 130012
Yan Wang* orcid id orcid.org/0000-0002-4751-0708 ; Jilin University, College of Computer Science and Technology, Changchun, CN 130012

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Abstract

The Gravitational Field Algorithm, a modern optimization algorithm, mainly simulates celestial mechanics and is derived from the Solar Nebular Disk Model (SNDM). It simulates the process of planetary formation to search for the optimal solution. Although this optimization algorithm has more advantages than other optimization algorithms in multi-peak optimization problems, it still has the shortcoming of long computation time when dealing with large-scale datasets or solving complex problems. Therefore, it is necessary to improve the efficiency of the Gravitational Field Algorithm (GFA). In this paper, an optimization method based on multi-population parallel is proposed to accelerate the Gravitational Field Algorithm. With the help of the parallel mechanism in MATLAB, the algorithm execution speed will be improved by using the parallel computing mode of multi-core CPU. In addition, this paper also improves the absorption operation strategy. By comparing the experimental results of eight classical unconstrained optimization problems, it is shown that the computational efficiency of this method is improved compared with the original Gravitational Field Algorithm, and the algorithm accuracy has also been slightly improved.

Keywords

Gravitational Field Algorithm; multi-population; parallel computing

Hrčak ID:

255805

URI

https://hrcak.srce.hr/255805

Publication date:

17.4.2021.

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