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

• DOI: 10.2478/jaiscr-2024-0018
• Publication Date: 2024-07-29T08:04:44Z
• Authors: Meng Yu; Mohammad Khishe; Leren Qian; Diego Martín; Laith Abualigah; Taher M. Ghazal
• Keywords: Optimization algorithm

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