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We proposed a new improved PSO\u2010ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites\u2019 energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO\u2010ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high\u2010quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO\u2010ACO algorithm, Min\u2013Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO\u2010ACO algorithm can plan a site energy dispatching route with shorter route, less time\u2010consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.<\/jats:p>","DOI":"10.1155\/2023\/3160184","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T02:20:27Z","timestamp":1676946027000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Energy Dispatching Based on an Improved PSO\u2010ACO 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