Jiang Huawei, Zhang Shulong, Guo Tao, Yang Zhen, Zhao Like, Zhou Yan, Zhou Dexiang
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
Math Biosci Eng. 2023 Jun 30;20(8):14414-14437. doi: 10.3934/mbe.2023645.
To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is proposed. First, an improved scan and Clarke-Wright algorithm is used to obtain the optimal vehicle path at the initial time. Then, the group movement strategy is designed to generate offspring individuals with an improved quality for refining the updating ability of individuals in the population. Finally, in order to maintain population diversity, a different weights strategy is used to expand individual search spaces, which can prevent individuals from prematurely gathering in a certain area. The experimental results show that the performance of the improved whale swarm algorithm is better than that of the ant colony system and the adaptive chaotic genetic algorithm, which can minimize the cost of material distribution and effectively eliminate the adverse effects caused by the change of road conditions.
为克服鲸鱼群算法在解决道路条件变化的物资应急调度问题时容易陷入局部极值的问题，提出了一种改进的鲸鱼群算法。首先，使用改进的扫描算法和克拉克 - 赖特算法在初始时刻获得最优车辆路径。然后，设计群体移动策略以生成质量更高的后代个体，从而提升种群中个体的更新能力。最后，为保持种群多样性，采用不同权重策略来扩展个体搜索空间，这可以防止个体过早聚集在某个区域。实验结果表明，改进的鲸鱼群算法的性能优于蚁群系统和自适应混沌遗传算法，它能够使物资配送成本最小化，并有效消除道路条件变化所带来的不利影响。
