Zaichang Zhou,
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smart city; Urban and rural planning; Power service nodes; Fairnessoptimization; Multi objective optimization; Improved genetic algo-rithm
In order to improve the spatial allocation efficiency of power service resources and the fairness between urban and rural areas in smart cities, this paper constructs a multi-objective optimization deploy- ment model for power service nodes oriented towards urban-rural planning. On the basis of analyzing the deployment constraints of power service nodes, this model integrates fairness indicators such as service radius, load balancing, and population density adaptation, and establishes an objective function system that balances fairness and service efficiency. By introducing Improved Genetic Algorithm (IGA) to solve the model, and conducting comparative experiments with Standard Genetic Algorithm (GA) and Particle Swarm Opti- mization (PSO). In the deployment simulation of typical urban and suburban areas in a certain city, IGA converged within 20 genera- tions, with a 12.6% increase in fairness indicators and a 9.4% increase in service coverage. The experimental results show that the model significantly optimizes the fairness of node distribution between ur- ban and rural areas while ensuring the overall efficiency of power services, providing theoretical support and technical path for opti- mizing spatial resource allocation in the context of smart cities.
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