Evolving chimp optimization algorithm using quantum mechanism for engineering applications: a case study on fire detection

Abstract This paper introduces the Quantum Chimp Optimization Algorithm (QU-ChOA), which integrates the Chimp Optimization Algorithm (ChOA) with quantum mechanics principles to enhance optimization capabilities. The study evaluates QU-ChOA across diverse domains, including benchmark tests, the IEEE CEC-06–2019 100-Digit Challenge, real-world optimization problems from IEEE-CEC-2020, and dynamic scenarios from IEEE-CEC-2022. Key findings highlight QU-ChOA’s competitive performance in both unimodal and multimodal functions, achieving an average success rate (SR) of 88.98% across various benchmark functions. QU-ChOA demonstrates robust global search abilities, efficiently finding optimal solutions with an average fitness evaluations (AFEs) of 14 012 and an average calculation duration of 58.22 units in fire detection applications. In benchmark tests, QU-ChOA outperforms traditional algorithms, including achieving a perfect SR of 100% in the IEEE CEC-06–2019 100-Digit Challenge for several functions, underscoring its effectiveness in complex numerical optimization. Real-world applications highlight QU-ChOA’s significant improvements in objective function values for industrial processes, showcasing its versatility and applicability in practical scenarios. The study identifies gaps in existing optimization strategies and positions QU-ChOA as a novel solution to these challenges. It demonstrates QU-ChOA’s numerical advancements, such as a 20% reduction in AFEs compared to traditional methods, illustrating its efficiency and effectiveness across different optimization tasks. These results establish QU-ChOA as a promising tool for addressing intricate optimization problems in diverse fields.