Quantum-inspired Evolutionary Algorithms - Models and Applications

Abstract

Quantum-inspired evolutionary algorithms (QIEAs) have emerged as a promising approach to optimization, leveraging principles from quantum computing to enhance the performance of evolutionary algorithms. This paper provides a comprehensive overview of QIEAs, discussing their underlying models and highlighting key applications across various domains. We first introduce the fundamental concepts of quantum computing and evolutionary algorithms, establishing the groundwork for understanding QIEAs. We then delve into the core models of QIEAs, including quantum-inspired representations, operators, and strategies. Next, we survey a range of applications where QIEAs have demonstrated significant improvements over traditional evolutionary algorithms, such as in combinatorial optimization, machine learning, and data clustering. Furthermore, we discuss the challenges and future directions of QIEAs, including scalability, parameter tuning, and hybridization with other optimization techniques. This paper aims to provide researchers and practitioners with a thorough understanding of QIEAs and inspire further advancements in this rapidly evolving field.