{"title":"Research on Unmanned Vehicle Path Planning based on Improved Bat Algorithm","authors":"Yizhu Jiang, Yefu Wu, Miao Wang","doi":"10.1109/DCABES57229.2022.00067","DOIUrl":null,"url":null,"abstract":"Vehicle driving technology has developed in the direction of informatization and intelligence, and the research hotspot is unmanned driving and path planning [1]. This paper proposes an improved bat algorithm (Improved Bat Algorithm, IBA). First, a weighted speed update mechanism is introduced in BA, so that with the increase of the number of iterations, the speed changes adaptively to avoid the algorithm falling into local extreme values prematurely. Then, in the subsequent iterations, the quadratic differential change mechanism is used to maintain the diversity of the bat population; and under the static road network, the optimal route is planned through the MAT LAB simulation experiment, which verifies that the IBA algorithm has better performance.","PeriodicalId":344365,"journal":{"name":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCABES57229.2022.00067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Vehicle driving technology has developed in the direction of informatization and intelligence, and the research hotspot is unmanned driving and path planning [1]. This paper proposes an improved bat algorithm (Improved Bat Algorithm, IBA). First, a weighted speed update mechanism is introduced in BA, so that with the increase of the number of iterations, the speed changes adaptively to avoid the algorithm falling into local extreme values prematurely. Then, in the subsequent iterations, the quadratic differential change mechanism is used to maintain the diversity of the bat population; and under the static road network, the optimal route is planned through the MAT LAB simulation experiment, which verifies that the IBA algorithm has better performance.
车辆驾驶技术正朝着信息化、智能化方向发展,无人驾驶和路径规划是研究热点[1]。本文提出了一种改进的蝙蝠算法(improved bat algorithm, IBA)。首先,引入加权速度更新机制,随着迭代次数的增加,速度自适应变化,避免算法过早陷入局部极值;然后,在后续的迭代中,利用二次微分变化机制来保持蝙蝠种群的多样性;在静态路网下,通过MAT LAB仿真实验规划出最优路线,验证了IBA算法具有较好的性能。