A Hybrid Quantum Particle Swarm Optimization based on Differential Evolution Strategy for Healthcare Applications

Optimization algorithms are crucial in solving complex problems in various domains, particularly healthcare reliability applications. QPSO has demonstrated promising results in optimization tasks due to its quantum-inspired search mechanisms. Still, it suffers from challenges such as premature convergence and poor exploration in high-dimensional spaces. To overcome these limitations, we introduce a new approach that combines QPSO with DE, resulting in an enhanced QPSO based on Differential Evolution Strategy (QPSO-DE). The QPSO-DE algorithm integrates the strengths of quantum mechanics with the robust evolutionary strategies of DE, thereby improving both local and global searching. The hybrid algorithm is applied to healthcare reliability problems, optimizing system parameters for efficient healthcare infrastructure management and minimizing failure risks in critical applications. Through extensive experiments, the proposed algorithm demonstrates significant improvements in convergence speed and solution quality compared to traditional optimization techniques. Results show that QPSO-DE outperforms other state-of-the-art methods, making it a promising tool for optimizing healthcare systems, medical equipment, and in - care strategies for patients. The study emphasizes the potential of hybrid optimization algorithms in solving real-world complex healthcare challenges, paving the way for more efficient and reliable healthcare systems.