Optimal Allocation of Water Resources Based on an Improved Quantum-Inspired Multi-objective Evolutionary Algorithm

Given the multi-objective properties of optimization models for regional water resource allocation, we analyzed the components and properties of water resource systems. This analysis allowed us to propose a globally optimized, quantum-inspired multi-objective algorithm (QMEA). This approach controls the balance and conformity among individual objectives using the overall degree of coordination of the ultimate goal. During the construction of the algorithm, we employed quantum coding for the discrimination of continuous variables to establish an evaluation function for the fitness of an individual variable, thereby integrating an evolutionary algorithm with quantum computing. Subsequently, we applied the QMEA for the optimal allocation of water resources. Our data indicate that this algorithm can effectively coordinate and constrain individual targets and address the goals of policymakers. In addition, this algorithm generates more objective results and is more user-friendly than other algorithms. As such, this algorithm provides an effective means to address multiple objectives for the optimal allocation of water resources.