基于改进鲸鱼优化算法的AGV多目标问题路径规划

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中图分类号：TP242.2 文献标识码：A

Path Planning for AGV Multi-objective Problem Based on Improved Whale Optimization Algorithm

LIU Yong，SUN Chuanzhu，FU Chaoxing （Collge of Mechanical and Electrical Engineering，Qingdao University，Qingdao 266071，China）

Abstract： To address the issue that traditional path planning algorithms cannot effectively solve the multi-objective problem of Automated Guided Vehicle （AGV） in path planning，the standard whale optimization algorithm is improved. Tent chaotic mapping and adaptive nonlinear dynamic inertia weight are introduced into the standard whale optimization algorithm，and the convergence factor and search coeficient are improved. Then，the improved algorithm is combined with the A ∗ algorithm for multi-objective point path planning. The iterative curve and running time of the improved whale optimization algorithm are tested using standard test functions， and a simulation comparison is conducted between the improved whale optimization algorithm and the standard whale optimization algorithm in the same map environment. The results show that，with a fixed population size，the improved whale optimization algorithm has a faster convergence speed and search accuracy compared to the standard whale optimization algorithm.

Keywords： path planning；multi-objective problem；whale optimization algorithm；AGV

随着科学技术的不断发展，AGV作为一种便捷的搬运设备，广泛应用于汽车制造、航空航天及港口物流等领域。（剩余5656字）