This model improves facility allocation and operational efficiency in competitive environments, indicating the integration of advanced optimization methods.
Key Points
The reinforcement learning-enhanced genetic algorithm improves early-stage convergence speed, enhancing optimization of bank locations.
The novel hybrid optimization method uses competitive decay functions to adapt and select optimal genetic operators in real time.
Experimental results show solution quality comparable to traditional methods, supporting the potential of intelligence-guided approaches in urban planning.
The algorithm's dynamic strategy utilizes an ε-greedy exploration mechanism, suggesting advancements in computational efficiency for facility allocation.