Research on Relocation Compensation and Whole Hospital Optimization Decision of Old Town Based on Particle Swarm Optimization Algorithm

The implementation of the "translational replacement" strategy in the renovation of existing bungalow blocks in the old city requires addressing issues such as relocation compensation, decision-making, cost-effectiveness judgment, and intelligent computing software framework design. This is a typical multi-objective optimization problem. Building models based on multiple algorithms to solve these problems can provide scientific relocation planning solutions for planning bureaus and developers, which has important practical significance. In the context of urbanization stock renewal, the impact of the "symbiotic courtyard" in the old city on redevelopment requires the design of a reasonable relocation compensation plan. Firstly, clarify the main factors and related constraints that affect residents' relocation, such as area, lighting, and repair. Assuming that residents' decisions are based on these factors and the rent remains unchanged for ten years. Using real estate economics and behavioral decision-making theory, the Logit model is used to quantify the probability of residents relocating, and the PSO algorithm is combined to optimize the model parameters. Obtain data through community research, fit Logit model parameters to determine compensation range, and consider factors such as spatial attributes and psychological price. Develop graded compensation plans for different residents and make dynamic adjustments. To maximize the economic benefits of the renovation of old city bungalow blocks, it is necessary to design relocation decisions based on compensation rules. The key is to maximize the number of vacant complete courtyards, adjacent to each other, and the total area while meeting the compensation rules. Considering factors such as plot attributes, courtyard relationships, costs, and time, it is assumed that residents follow compensation rules, plot courtyard attributes, and fixed development cycle costs. Using operations research and graph theory, a two-stage heuristic algorithm is adopted. Firstly, the "key residents" are determined through greedy search to generate an initial plan, and then the plan is optimized using genetic algorithm. Establish the objective function and constraints, and solve for the relocation plan as well as the costs, benefits, and other results.