Sunita Salunke
Centre of Interdisciplinary Studies and Research, D. Y. Patil International University, Akrudi, 411044, Pune, Maharashtra, India.

Anuj Kumar
School of Computer Science Engineering and Applications, D. Y. Patil International University, Akrudi, 411044, Pune, Maharashtra, India.

Sangeeta Pant
Symbiosis Institute of Technology, Pune, Symbiosis International (Deemed University), Lavale, 412115, Pune, Maharashtra, India.

Manoj K. Singh
School of Computer Science Engineering and Technology, Bennett University, Techzone II, 201310, Greater Noida, Uttar Pradesh, India.

DOI https://doi.org/10.33889/IJMEMS.2026.11.4.075

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Abstract

Quantum-inspired metaheuristic approaches, often called QIMAs, basically try to mix quantum computing ideas with the usual metaheuristic search routines. In practice, they make it easier to go through tangled search spaces, and they also help maintain a broader population variety, thanks to probabilistic qubit style encodings. Because of that, these methods have drawn a lot of attention across multiple academic directions, like materials science and engineering optimization, plus areas such as drug development and cryptography. QIMAs deliver a decent alternative by stitching Quantum-inspired mechanisms into algorithms that can run on ordinary classical machines, even if actually building full universal quantum computers is still hard, because quantum states are fragile and need heavy error correction, also the hardware is just plain complicated. Theoretical underpinnings, algorithmic progress and real-world applications of QIMAs are all looked at in depth in this article, sort of in a grounded way. The literature covering primarily 2015 to 2025 is reviewed carefully, then organized by the kind of inspiration it uses, like evolutionary algorithms, swarm intelligence approaches, and also genetic algorithms that are inspired by quantum mechanics in a more direct sense. The studies we examine show quite clearly how QIMAs can be used effectively for things like structural design, image processing, flight control, feature selection, and predictive modeling. The intention behind this review is to offer scholars and practitioners a complete, operationally efficient resource on Quantum-inspired optimization methods, for tackling tricky optimization problems.

Keywords- NP-hard problems, Global optimization, Metaheuristic algorithms, Quantum computing, Quantum-inspired metaheuristic algorithms.

Citation

Salunke, S., Kumar, A., Pant, S., & Singh, M. K (2026). A Comprehensive Review on Quantum-inspired Metaheuristic Algorithms: Basics to Recent Advancements. International Journal of Mathematical, Engineering and Management Sciences, 11(4), 1863-1906. https://doi.org/10.33889/IJMEMS.2026.11.4.075.