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Review
. 2022 Oct 23:1-16.
doi: 10.1007/s12065-022-00783-2. Online ahead of print.

A review of recent advances in quantum-inspired metaheuristics

Shahin Hakemi  1 , Mahboobeh Houshmand  1 , Esmaeil KheirKhah  1 , Seyyed Abed Hosseini  2
Affiliations
Review

A review of recent advances in quantum-inspired metaheuristics

Shahin Hakemi et al. Evol Intell. .

Abstract

Quantum-inspired metaheuristics emerged by combining the quantum mechanics principles with the metaheuristic algorithms concepts. These algorithms extend the diversity of the population, which is a primary key to proper global search and is guaranteed using the quantum bits' probabilistic representation. In this work, we aim to review recent quantum-inspired metaheuristics and to cover the merits of linking the quantum mechanics notions with optimization techniques and its multiplicity of applications in real-world problems and industry. Moreover, we reported the improvements and modifications of proposed algorithms and identified the scope's challenges. We gathered proposed algorithms of this scope between 2017 and 2022 and classified them based on the sources of inspiration. The source of inspiration for most quantum-inspired metaheuristics are the Genetic and Evolutionary algorithms, followed by swarm-based algorithms, and applications range from image processing to computer networks and even multidisciplinary fields such as flight control and structural design. The promising results of quantum-inspired metaheuristics give hope that more conventional algorithms can be combined with quantum mechanics principles in the future to tackle optimization problems in numerous disciplines.

Keywords: Global optimization; Metaheuristics; NP-hard problems; Optimization techniques; Quantum computing; Quantum-inspired algorithms.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Rotation operation for qubit
Fig. 2
Fig. 2
Metaheuristics major categories
Fig. 3
Fig. 3
Distribution of quantum-inspired metaheuristics’ source of inspiration from 2017 to 2022
Fig. 4
Fig. 4
Flowchart of QGA [13]
Fig. 5
Fig. 5
Flowchart of QEA [24]
Fig. 6
Fig. 6
Flowchart of QPSO [59]
See this image and copyright information in PMC

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