摘要
为解决传统子区域覆盖路径规划方法的环境普适性不足等问题,课题组设计了专门的头脑风暴-遗传算法BSO-GA。对原始头脑风暴算法个体更新方式进行了改进,单个个体更新采用遗传算法移位、倒位和换位算子的思想,混合个体更新采用贪心交叉算子。实验结果表明:BSO-GA在距离、运行时间上均优于头脑风暴算法、遗传算法、模拟退火算法和遗传-模拟退火算法;无论在普通作业环境还是特殊作业环境,该算法覆盖率均能达到100%,且没有路径交叉及重复现象,能够较好地完成覆盖任务。
To solve the problem of the lack of universality of the traditional sub-area coverage path planning method to the environment,a special brain storm optimization-genetic algorithm was designed.The individual update method of the original brain storm optimization was improved.The update of single individual adopted the idea of genetic algorithm shift,inversion and transposition operator,and the update of mixed individual adopted the greedy crossover operator.The experimental results show that the brain storm optimization-genetic algorithm is better than brain storm optimization,genetic algorithm,simulated annealing and genetic-simulated annealing in distance and running time.The coverage rate of this algorithm can reach 100%,no matter in normal working environment or special working environment,there is no path crossing and repetition phenomenon,and the coverage task can be completed well.
作者
董雅文
杨静雯
刘文慧
张宝锋
DONG Yawen;YANG Jingwen;LIU Wenhui;ZHANG Baofeng(School of Mechanical and Electrical Engineering,Xi′an Polytechnic University,Xi′an 710048,China;School of Mechanical&Precision Instrumental Engineering,Xi′an University of Technology,Xi′an 710048,China)
出处
《轻工机械》
CAS
2021年第6期57-64,共8页
Light Industry Machinery
基金
陕西省教育厅专项科研计划项目:突发事件下应急医疗资源优化调度与配送问题研究(18JK0324)。
