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

Abstract

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.