WOLF COLONY IMPROVEMENT ALGORITHM IN INTELLIGENT LOGISTICS SUPERVISION SYSTEM

Taizhen Zhang and Jianhui Xu

Keywords

Intelligent optimisation algorithm, wolf colony algorithm, logistics, supervision system, adaptive weight

Abstract

With the rapid advancement of the logistics industry, intelligent logistics (IL) technology has become a research hotspot for scholars. Among them, the wolf colony algorithm (WCA), as a new intelligent optimisation algorithm, has received widespread attention, but because of its short time, it still has the problem of low computational efficiency. Therefore, the research first carried out the software and hardware design of the IL supervision system, and then improved the calculation methods of walking, summoning and besieging behaviours in the WCA to improve the global convergence and computing efficiency of the algorithm. Finally, during the attacking process, adaptive weights (AWs) are added to automatically adjust the step size, and a improved wolf swarm algorithm is designed and applied to the IL supervision system. The research outcomes demonstrate the variance of the improved algorithm is 7.7826 × 10−9 and 1.6144 × 10-14, respectively, and its stability is better. In the iterative convergence curve, the improved algorithm has a faster convergence speed, with a shortest path of 15574.58 and higher accuracy. In the calculation of the optimal value of logistics path, the improved algorithm has a value of 5.31 × 105, which is superior to other algorithms. This indicates that the algorithm has high stability, efficiency, and accuracy, providing better technical support for IL supervision systems.

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