A Quantum-Inspired Predator–Prey Algorithm for Real-Parameter Optimization

Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator–p...

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Bibliographic Details
Published inAlgorithms Vol. 17; no. 1; p. 33
Main Authors Khan, Azal Ahmad, Hussain, Salman, Chandra, Rohitash
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2024
Subjects
Algorithms
Comparative analysis
discrete optimization
Evolution
Foraging behavior
Genetic algorithms
Linear programming
Mathematical optimization
metaheuristic optimization
Optimization
Optimization algorithms
Optimization techniques
Parameters
Predators
Quantum computing
Quantum physics
Germany
Online AccessGet full text
ISSN1999-4893
1999-4893
DOI10.3390/a17010033

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Summary:Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator–prey algorithm (QPPA), which fuses the fundamentals of quantum computing and swarm optimization based on a predator–prey algorithm. Our results demonstrate the efficacy of QPPA in solving complex real-parameter optimization problems with better accuracy when compared to related algorithms in the literature. QPPA achieves highly rapid convergence for relatively low- and high-dimensional optimization problems and outperforms selected traditional and advanced algorithms. This motivates the application of QPPA to real-world application problems.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a17010033