MAR-GSA: Mixed attraction and repulsion based gravitational search algorithm

As a population-based stochastic optimization algorithm, Gravitational Search Algorithm (GSA) has attracted numerous interests and has been applied in various applications. However, GSA has drawbacks such as uneven search and premature convergence in practical applications. This paper specifically e...

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Bibliographic Details
Published in:Information sciences Vol. 662; p. 120250
Main Authors: Qian, Zhiqiang, Xie, Yongfang, Xie, Shiwen
Format: Journal Article
Language:English
Published: Elsevier Inc 01.03.2024
Subjects:
Center-biased search characteristic
Gravitational search algorithm
Mixed attraction and repulsion
Population-based algorithm
Shift transformation
Shift transformation
Gravitational search algorithm
Center-biased search characteristic
Mixed attraction and repulsion
Population-based algorithm
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:As a population-based stochastic optimization algorithm, Gravitational Search Algorithm (GSA) has attracted numerous interests and has been applied in various applications. However, GSA has drawbacks such as uneven search and premature convergence in practical applications. This paper specifically explains the inherent characteristic of GSA in prioritizing the center position. Correspondingly, an improvement strategy of fitness normalization with mass shift is proposed, creating a situation where gravity and repulsion are mixed. Then, the global best mechanism with weights is incorporated into the particle's velocity update formula, which compensates for the difficulties in the later exploitation stage. Finally, an empirical formula for the initial gravitational constant related to the size of the solution space is proposed, which enhances the global search ability together with the former strategy. 12 shifted benchmark functions are used to construct 20 optimization problems ranging from 2 to 120 dimensions. The average performance of the proposed algorithm, other GSA and well-known algorithms are compared under the same budget. The results demonstrate that the proposed GSA not only effectively addresses the drawbacks of GSA and maintains good performance, but also exhibits strong competitiveness compared to various similar algorithms.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.120250