Quantum-inspired metaheuristic algorithms: comprehensive survey and classification

Metaheuristic algorithms are widely known as efficient solutions for solving problems of optimization. These algorithms supply powerful instruments with significant engineering, industry, and science applications. The Quantum-inspired metaheuristic algorithms were developed by integrating Quantum Computing (QC) concepts into metaheuristic algorithms. The QC-inspired metaheuristic algorithms solve combinational and numerical optimization problems to achieve higher-performing results than conventional metaheuristic algorithms. The QC is used more than any other strategy for accelerating convergence, enhancing exploration, and exploitation, significantly influencing metaheuristic algorithms' performance. The QC is a new field of research that includes elements from mathematics, physics, and computing. QC has attracted increasing attention among scientists, technologists, and industrialists. During the current decade, it has provided a research platform for the scientific, technical, and industrial areas. In QC, metaheuristic algorithms can be improved by the parallel processing feature. This feature helps to find the best solutions for optimization problems. The Quantum-inspired metaheuristic algorithms have been used in the optimization fields. In this paper, a review of different usages of QC in metaheuristics has been presented. This review also shows a classification of the Quantum-inspired metaheuristic algorithms in optimization problems and discusses their applications in science and engineering. This review paper's main aims are to give an overview and review the Quantum-inspired metaheuristic algorithms applications.

Automated summary

Metaheuristic algorithms are efficient solutions for optimization problems, and quantum-inspired metaheuristic algorithms, which integrate quantum computing concepts, can achieve better results in solving complex optimization problems. Quantum computing plays a crucial role in enhancing the performance of metaheuristic algorithms.