Population interaction network in representative gravitational search algorithms: Logistic distribution leads to worse performance

In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the partic...

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Veröffentlicht in:Heliyon Jg. 10; H. 11; S. e31631
Hauptverfasser: Li, Haotian, Yang, Yifei, Wang, Yirui, Li, Jiayi, Yang, Haichuan, Tang, Jun, Gao, Shangce
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 15.06.2024
Elsevier BV
Elsevier
Schlagworte:
algorithms
Complex network
Cumulative distribution
Gravitational search algorithm
H1-99
Meta-heuristic algorithms
Poisson distribution
Population interaction network
Q1-390
Research Article
Science (General)
Social sciences (General)
Gravitational search algorithm
Cumulative distribution
Population interaction network
Meta-heuristic algorithms
Complex network
ISSN:2405-8440, 2405-8440
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Zusammenfassung:In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the particle swarm optimization algorithm (PSO), the gravitational search algorithm (GSA), and four classical variations of the gravitational search algorithm, are analyzed in terms of inter-individual information interactions and the differences in the performance of each of the algorithms on IEEE Congress on Evolutionary Computation 2017 benchmark functions. The cumulative distribution function (CDF) of the node degree obtained by the algorithm on the benchmark function is fitted to the seven distribution models by using PIN. The results show that among the seven compared algorithms, the more powerful DE is more skewed towards the Poisson distribution, and the weaker PSO, GSA, and GSA variants are more skewed towards the Logistic distribution. The more deviation from Logistic distribution GSA variants conform, the stronger their performance. From the point of view of the CDF, deviating from the Logistic distribution facilitates the improvement of the GSA. Our findings suggest that the population interaction network is a powerful tool for characterizing and comparing the performance of different MHAs in a more comprehensive and meaningful way. •Population interaction networks for seven MHAs are established.•Seven distribution models are used to fit the frequency of degrees of nodes.•The CDF of node degrees for classic PSO conforms to the Logistic distribution.•The CDF of node degrees for GSA and variants conforms to the Logistic distribution.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e31631