Improvement of White Shark Algorithms Combining Logistic Maps and Gaussian Variations for Underground Logistics Network System Optimization
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The planning of underground logistics pipeline networks is a crucial component of urban underground logistics systems, aiming to find the optimal construction path for the logistics network, improve logistics efficiency, and reduce operational costs. However, due to the complexity and uncertainty of the underground environment, traditional planning methods often fall short. This paper proposes a improved underground logistics pipeline network planning method based on the White Shark Optimization(WSO) algorithm, referred to as LGWSO(White Shark Algorithms Combining Logistic Maps and Gaussian Variations). The proposed method first establishes an underground space model and then uses the LGWSO algorithm for path planning. By adopting chaos initialization method and Gaussian mutation strategy, the performance of the algorithm has been effectively improved. Through simulation experiments, the algorithm has demonstrated significant advantages in optimization accuracy, convergence speed, and robustness. Compared to traditional planning methods, the proposed approach is better suited to handle the complex underground environment, providing an optimized strategy for the construction of urban logistics system underground networks.
Abstract
The planning of underground logistics pipeline networks is a crucial component of urban underground logistics systems, aiming to find the optimal construction path for the logistics network, improve logistics efficiency, and reduce operational costs. However, due to the complexity and uncertainty of the underground environment, traditional planning methods often fall short. This paper proposes a improved underground logistics pipeline network planning method based on the White Shark Optimization(WSO) algorithm, referred to as LGWSO(White Shark Algorithms Combining Logistic Maps and Gaussian Variations). The proposed method first establishes an underground space model and then uses the LGWSO algorithm for path planning. By adopting chaos initialization method and Gaussian mutation strategy, the performance of the algorithm has been effectively improved. Through simulation experiments, the algorithm has demonstrated significant advantages in optimization accuracy, convergence speed, and robustness. Compared to traditional planning methods, the proposed approach is better suited to handle the complex underground environment, providing an optimized strategy for the construction of urban logistics system underground networks.
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