Atom search optimization: a systematic review of current variants and applications

Atom search optimization (ASO) is a renowned physics-based metaheuristic algorithm which takes inspiration from the fundamental theory of molecular dynamics and imitates the natural atomic movement characteristics in conformity with classical mechanics. ASO has demonstrated its preeminence over many...

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Vydáno v:Knowledge and information systems Ročník 67; číslo 6; s. 4813 - 4914
Hlavní autoři: Mugemanyi, Sylvère, Qu, Zhaoyang, Rugema, François Xavier, Dong, Yunchang, Wang, Lei, Mutuyimana, Félicité Pacifique, Mutabazi, Emmanuel, Habumuremyi, Providence, Mutabazi, Rita Clémence, Muhirwa, Alexis, Bananeza, Christophe, Nshimiyimana, Arcade, Kagaju, Clarisse, Nsengumuremyi, Jean
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.06.2025
Springer Nature B.V
Témata:
Algorithms
Classical mechanics
Computer Science
Computing time
Data Mining and Knowledge Discovery
Database Management
Error analysis
Heuristic methods
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Molecular dynamics
Optimization
Performance indices
Review
Statistical analysis
Variance analysis
Exploration
Metaheuristic
Swarm intelligence
Atom search optimization
Physics-based algorithm
Exploitation
ISSN:0219-1377, 0219-3116
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Shrnutí:Atom search optimization (ASO) is a renowned physics-based metaheuristic algorithm which takes inspiration from the fundamental theory of molecular dynamics and imitates the natural atomic movement characteristics in conformity with classical mechanics. ASO has demonstrated its preeminence over many well-known algorithms in solving real-world optimization problems and flexible implementation; hence, it has great recognition in academic communities. Due to this rising attraction, numerous modifications and improved variants of ASO have been designed and ASO finds applications in many fields including engineering, computer science, medicine, health sector, etc. Therefore, this study aims at reviewing and analyzing the ASO along with its variants. In this review, many reported studies based on ASO have been identified, collected, discussed and summed up. We have identified and selected 141 ASO-related papers published in recognized journals. The strengths as well as weaknesses of ASO have been discussed. The performance of ASO and its selected variants in convergence analysis was evaluated on 23 benchmark test functions with dimension 30. Furthermore, statistical analysis was performed to rank and validate the performance of ASO and its variants. The results reveal that QASO exhibits better overall effectiveness of 91.304% and ranks first in dimension 30 based on both Friedman and Kruskal–Wallis tests. The results also illustrate that the conventional ASO is the fastest algorithm amidst the compared algorithms in terms of computational time. In addition, the results show that the ESA-ASO achieves better results regarding the performance index and mean absolute error test. According to analysis of variance (ANOVA) test, Tukey HSD (honestly significant difference) post hoc test and Kruskal–Wallis test, the findings demonstrate that there are no statistically significant differences between ASO and its variants. Finally, the prospects for future trends are suggested.
Bibliografie:ObjectType-Article-1
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-025-02389-3