Quantum Chimp Optimization Algorithm : a novel integration of quantum mechanics into the chimp optimization framework for enhanced performance

• Autorzy: Yu Meng , Khishe Mohammad , Qian Leren , Martín Diego , Abualigah Laith , Ghazal Taher M.

Identyfikatory

DOI

10.2478/jaiscr-2024-0018

• Języki publikacji: EN

Abstrakty

EN

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 10.2478/jaiscr-2024-0018 – 359 322 Meng Yu, Mohammad Khishe, Leren Qian, Diego Mart´ın, Laith Abualigah, Taher M. Ghazal 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.

Słowa kluczowe

EN

statistical analysis; engineering application; Chimp optimization algorithm; quantum optimization

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2024
• Tom: Vol. 14, No. 4
• Strony: 321--359
• Opis fizyczny: Bibliogr. 97 poz., rys.

Twórcy

autor

Yu Meng

• School of Artificial Intelligence, Anshan Normal University, Anshan, 114005, China

• https://orcid.org/0009-0003-1219-5423

autor

Khishe Mohammad

• Department of Electronic Engineering, Imam Khomeini Naval Science University of Nowshahr, Iran
• Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan
• Innovation Center for Artificial Intelligence Applications, Yuan Ze University, Taiwan

• https://orcid.org/0000-0002-1024-8822

autor

Qian Leren

• School of Computing and Augmented Intelligence, Arizona State University, Tempe 85281, AZ, USA

autor

Martín Diego

• ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain

• https://orcid.org/0000-0001-8810-0695

autor

Abualigah Laith

• Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan
• MEU Research Unit, Middle East University, Amman 11831, Jordan
• Jadara Research Center, Jadara University, Irbid 21110, Jordan
• Centre for Research Impact & Outcome, Chitkara University, Punjab, India

• https://orcid.org/0000-0002-2203-4549

autor

Ghazal Taher M.

• Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia

• https://orcid.org/0000-0003-0672-7924

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).