Research on distribution network planning and allocation optimization method based on genetic taboo hybrid algorithms

Abstract Distribution network planning and allocation optimization is the key to improving the operational performance of distribution networks. This study focuses on the optimization method of distribution network planning and allocation based on a genetic taboo hybrid algorithm. Firstly, we collect and process node, line, and power information, determine the objective function and constraints, then initialize the population and taboo table and generate the initial solution. In genetic algorithm operations, population evolution is achieved through fitness evaluation, selection, crossover, and mutation. In taboo search algorithm operations, individual local optimization is carried out through neighborhood search, taboo judgment, and updating. Hybrid algorithms integrate the advantages of both, achieving a balance between global and local search through individual replacement and population updating. The experimental results show that the hybrid algorithm performs better than traditional algorithms in reducing costs, minimizing network losses, improving power supply reliability, and accelerating convergence speed, providing an effective solution for distribution network planning and allocation optimization.