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<p>This research introduces the Quantum Chimp Optimization Algorithm (QChOA), a pioneering methodology that integrates quantum mechanics principles into the Chimp Optimization Algorithm (ChOA). By incorporating non-linearity and uncertainty, the QChOA significantly improves the ChOA’s exploration and exploitation capabilities. A distinctive feature of the QChOA is its ability to displace a ’chimp,’ representing a potential solution, leading to heightened fitness levels compared to the current top search agent. Our comprehensive evaluation includes twenty- nine standard optimization test functions, thirty CEC-BC functions, the CEC06 test suite, ten real-world engineering challenges, and the IEEE CEC 2022 competition’s dynamic optimization problems. Comparative analyses involve four ChOA variants, three leading quantum-behaved algorithms, three state-ofthe-art algorithms, and eighteen benchmarks. Employing three non-parametric statistical tests (Wilcoxon rank-sum, Holm-Bonferroni, and Friedman average rank tests), results show that the QChOA outperforms counterparts in 51 out of 70 scenarios, exhibiting performance on par with SHADE and CMA-ES, and statistical equivalence to jDE100 and DISHchain1e+12. The study underscores the QChOA’s reliability and adaptability, positioning it as a valuable technique for diverse and intricate optimization challenges in the field.</p>
</abstract>

This research introduces the Quantum Chimp Optimization Algorithm (QChOA), a pioneering methodology that integrates quantum mechanics principles into the Chimp Optimization Algorithm (ChOA). By incorporating non-linearity and uncertainty, the QChOA significantly improves the ChOA’s exploration and exploitation capabilities. A distinctive feature of the QChOA is its ability to displace a ’chimp,’ representing a potential solution, leading to heightened fitness levels compared to the current top search agent. Our comprehensive evaluation includes twenty- nine standard optimization test functions, thirty CEC-BC functions, the CEC06 test suite, ten real-world engineering challenges, and the IEEE CEC 2022 competition’s dynamic optimization problems. Comparative analyses involve four ChOA variants, three leading quantum-behaved algorithms, three state-ofthe-art algorithms, and eighteen benchmarks. Employing three non-parametric statistical tests (Wilcoxon rank-sum, Holm-Bonferroni, and Friedman average rank tests), results show that the QChOA outperforms counterparts in 51 out of 70 scenarios, exhibiting performance on par with SHADE and CMA-ES, and statistical equivalence to jDE100 and DISHchain1e+12. The study underscores the QChOA’s reliability and adaptability, positioning it as a valuable technique for diverse and intricate optimization challenges in the field.

Quantum Chimp Optimization Algorithm: A Novel Integration of Quantum Mechanics Into the Chimp Optimization Framework for Enhanced Performance

Imam Khomeini Naval Science University of Nowshahr

ETSI Telecomunicación, Universidad Politécnica de Madrid

MEU Research Unit, Middle East University

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This research introduces the Quantum Chimp Optimization Algorithm (QChOA), a pioneering methodology that integrates quantum mechanics principles into the Chimp...

Quantum Chimp Optimization Algorithm: A Novel Integration of Quantum Mechanics Into the Chimp Optimization Framework for Enhanced Performance

Online veröffentlicht: 29. Juli 2024

Seitenbereich: 321 - 359

Eingereicht: 07. Feb. 2024

Akzeptiert: 01. Juli 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0018

Schlüsselwörter
statistical analysis, engineering application, Chimp optimization algorithm, quantum optimization

© 2024 Meng Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Quantum Chimp Optimization Algorithm: A Novel Integration of Quantum Mechanics Into the Chimp Optimization Framework for Enhanced Performance

Online veröffentlicht: 29. Juli 2024

Seitenbereich: 321 - 359

Eingereicht: 07. Feb. 2024

Akzeptiert: 01. Juli 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0018

Schlüsselwörterstatistical analysis, engineering application, Chimp optimization algorithm, quantum optimization

© 2024 Meng Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
statistical analysis, engineering application, Chimp optimization algorithm, quantum optimization