Exploration of Urban Ecological Planning and Urbanization Balance Model Driven by Simulated Annealing Algorithm

Abstract

Maintaining ecological balance and development has been one of the biggest challenges in the field of urban planning. This paper seeks to achieve the optimal balance between urban ecological planning and urbanization through SA algorithm. The study first constructs a comprehensive evaluation model for the balance between the urban ecological planning and urbanization, which includes multiple dimensions such as economic development, environment protection, resource utilization and social welfare. There after SA algorithm is introduced as an optimization tool for finding the optimal solution of the model by simulating the energy changes and the state transitions during the solid annealing. This paper specifies the initial temperature of the algorithm, and generates an initial urban planning scheme at random as starting point. Subsequently, the neighborhood solutions are produced by changing the parameters in the scheme. When evaluating these neighborhood solutions, these are compared by the value of the objective function and then a decision of accepting a new solution which is inferior to the current solution is made by the Metropolis criterion. The temperature is slowly decreased in the iteration process so that the algorithm slowly converges to an approximate optimal solution. The SA algorithm succeeds in detecting the best scheme of urban planning with the average green coverage rate of 38.81%, building density values range primarily in [0.2, 0.4] and the traffic flow becomes higher as compared to PSO algorithm. These optimization results not only greatly improve the comprehensive benefits of the city but also provide a scientific basis for the sustainable development of the city.