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Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy
Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy
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Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy
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Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy
Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy

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Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy
Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy
Journal Article

Optimization of A comprehensive dispatching system based on ant colony algorithm and dynamic weight power dispatching strategy

2025
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Overview
In this study, we explored an optimization method for a comprehensive power dispatching system based on the fusion of ant colony algorithm and dynamic weight scheduling strategy. Firstly, the limitations of the existing scheduling system are introduced. Then, the proposed optimization methods are elaborated in detail, including the basic principle of ant colony algorithm, the design of dynamic weight scheduling strategy, and the fusion mode of the two. A large number of experimental data prove that this method is superior to the traditional scheduling method. Experimental results show that the integrated scheduling system optimization method based on ant colony algorithm and dynamic weight scheduling strategy significantly improves the scheduling efficiency and resource utilization. Specifically, the method reduces the average dispatch time by 20% and improves the resource utilization by 15% when dealing with large-scale power dispatching problems. This indicates that the method has high practical value and can provide strong support for the optimization of scheduling system in related fields.