摘要
针对现有基于粒子群参数优化的改进蚁群算法耗时较大的问题,提出了一种新的解决方案.方案中采用一种全局异步与精英策略相结合的信息素更新方式,同时合理减少蚁群算法被粒子群算法调用一次所需的迭代代数.对日本旭川垃圾场巡查机器人路径规划问题仿真求解的结果表明,与其他算法相比,该改进算法具有比较明显的速度优势.
This article introduces a novel algorithm to solve the large time-consuming problem of the existing improved ant colony optimization （ACO） based on particle swarm optimization （PSO）. A new pheromone update method which combines the global asynchronous feature and elitist strategy was used in the algorithm. Moreover, the iteration steps of ACO invoked by PSO were reasonably reduced. The algorithm was applied to solve the path planning problem of landfill inspection robots in Asahikawa, Japan. It is shown that the algorithm has a better performance in search speed compared with other algorithms recently reported.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2013年第7期955-960,共6页
Journal of University of Science and Technology Beijing
基金
教育部第36批"留学回国人员科研启动基金"资助项目(1341)
国家自然科学基金资助项目(60374032)
北京市重点学科建设项目(XK100080537)
关键词
粒子群算法
蚁群算法
机器人
路径规划
旅行商问题
particle swarm optimization
ant colony optimization
robots
path planning
traveling salesmanproblem
