Quantum Computing-Inspired Genetic Algorithm for Network Optimization in WSN
Keywords:
Quantum computing, genetic algorithm, network optimization, wireless sensor network (WSN), quantum-inspired routingAbstract
This study presents a pioneering Quantum Computing-Inspired Genetic Algorithm (QIGA) designed for the efficient optimization of Wireless Sensor Networks (WSN). Leveraging the principles of quantum computing, QIGA employs a unique approach to address the complex routing challenges in WSNs. The algorithm starts with the quantum encoding of candidate routes, utilizing quantum bits (qubits) to represent multiple routes simultaneously through principles like superposition and entanglement. Genetic operations, including crossover and mutation, are then applied in the quantum domain to explore diverse solution spaces. The quantum-encoded routes are subsequently decoded into classical routes, and their fitness is evaluated based on crucial WSN optimization criteria, such as energy efficiency, latency, and reliability. The study integrates quantum-inspired selection strategies to determine the next generation of routes, fostering adaptability and efficiency in the optimization process. Through iterative refinement, QIGA aims to converge towards optimal routing solutions for WSNs. The proposed algorithm showcases a quantum-inspired paradigm that holds promise for addressing the intricate challenges of network optimization in WSNs. The study contributes to the evolving landscape of quantum computing applications in networking and lays the foundation for future advancements in quantum-inspired algorithms tailored for practical implementation in WSN environments.
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