一种改进粒子群通讯算法在目标搜索中的应用
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摘要
针对粒子群算法应用于机器人目标搜索过程中存在的早熟现象,提出一种基于改进粒子群算法和模拟退火算法相结合的目标搜索新方法,以提高算法的全局搜索能力。为解决通讯距离有限、机器人无法与基站进行信息交互和不能实时追踪动态目标等问题,引入通讯功能。算法中机器人与基站有两种通讯方式,一种是基站跟随最优机器人移动的通讯方式,另一种是在前者基础上将机器人按一定比例分为通讯机器人和搜索机器人的通讯方式,由通讯机器人负责搜索机器人与基站之间的通讯。两种通讯方式下机器人都采用动态多目标搜索策略搜索动态多目标。在考虑通讯距离的情况下,经过仿真测试,与传统的通讯粒子群算法相比,提出的改进通讯粒子群算法能更加有效地追踪动态目标。
Aiming at the prematurity of swarm optimization(PSO)algorithm when applied to the robot target search process,a new target search method based on improved particle swarm optimization algorithm and simulated annealing algorithm is proposed. For the premature phenomenon of particle swarm optimization algorithm,the simulated annealing algorithm is introduced to improve the algorithm of the global search capability. In order to tackle the problem that the robot can not have information interaction with the base station and real-time dynamic target tracking due to the limited communication distance,this paper introduces the communication function.Algorithm of robot and base station has two kinds of communication methods,one is the base station with the optimal robot of mobile communication,the other is based on the former general robot according to certain proportion into the communication robot communication methods and search robot,the robot communications is responsible for the search robot and communications between the base station. Robots are under two kinds of communication methods using dynamic multi-objective search strategy. Under the condition of considering the communication distance,compared with the traditional communication particle swarm optimization algorithm,the improved communication particle swarm optimization algorithm can track dynamic targets more effectively according to the simulation test.
Aiming at the prematurity of swarm optimization(PSO)algorithm when applied to the robot target search process,a new target search method based on improved particle swarm optimization algorithm and simulated annealing algorithm is proposed. For the premature phenomenon of particle swarm optimization algorithm,the simulated annealing algorithm is introduced to improve the algorithm of the global search capability. In order to tackle the problem that the robot can not have information interaction with the base station and real-time dynamic target tracking due to the limited communication distance,this paper introduces the communication function.Algorithm of robot and base station has two kinds of communication methods,one is the base station with the optimal robot of mobile communication,the other is based on the former general robot according to certain proportion into the communication robot communication methods and search robot,the robot communications is responsible for the search robot and communications between the base station. Robots are under two kinds of communication methods using dynamic multi-objective search strategy. Under the condition of considering the communication distance,compared with the traditional communication particle swarm optimization algorithm,the improved communication particle swarm optimization algorithm can track dynamic targets more effectively according to the simulation test.
引文
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[2] KENNEDY J,EBERHART R C. Particle swarm optimization.proceedings[J]. IEEE International Conference on Neural Networks,1995(4):1942-1948.
[3] EBERHART R,KENNEDY J. A new optimizer using particle swarm theory[C].Proceeding of the sixth international symposium on Micro Machine and Human Science,1995:39-43.
[4] COLORNI A,DORIGO M.Distributed optimization by ant colonies[C].Proceeding of the First European Conference on Artificial Life,1991:134-142.
[5] DORIGO M. Optimization,learning and natural algorithms[C]. Dissertation.Dipartimentodi Elettronica,Politecnico di Milano,Italy,1992.
[6] PERREAULT L,WITTIE M P,SHEPPARD J. Communication-aware distributed PSO for dynamic robotic search[C]. Swarm Intelligence.IEEE,2014:1-8.
[7] LEE J,CHO K,LEE S,et al. Distributed and energy-efficient target localization and tracking in wireless sensor networks[J]. Computer Communications,2006,29(13):2494-2505.
[8] HEREFORD J M,SIEBOLD M,NICHOLS S. Using the particle swarm optimization algorithm for robotic search applications[C].2007IEEE Swarm Intelligence Symposium. IEEE,2007:53-59.
[9] PUGH J,MARTINOLI A. Distributed adaptation in multi-robot search using particle swarm optimization[C].International Conference on Simulation of Adaptive Behavior,2008:393-402.
[10] CAI Y,YANG S X. A potential field-based pso approach for cooperative target searching of multi-robots[C]. Intelligent Control and Automation. IEEE,2015:1029-1034.
[11] KENNEDY J. Why does it need velocity[C]. Swarm Intelligence Symposium,2005:38-44.
[12] SHI Y H,EBERHART R C. A modified particle swarm optimizer[C].Proceeding of the IEEE International Conference on Evolutionary Computation,1997:303-308.
[13]康立山.非数值并行算法——模拟退火算法[M].北京:科学出版社,1997:22-55.
[14] DANTZING G,RAMSER J. The truck is patching problem[J]. Management science,1959(6):80-91.
[15] TIKANM,KI A,KEL,et al. Multi-robot system for exploration in an outdoor environment[C]. International Conference on Robotics and Applications,2007:470-476.
[16]赵志刚,黄树运,王伟倩.基于随机惯性权重的简化粒子群优化算法[J].计算机应用研究,2014,31(2):361-363.
[17] JOHNSON D S,ARAGON C R,MCGEOCH L A,et al. Optimization by simulated annealing:an experimental evaluation;part III[J]. Science,1990,39(2548):671-680.
[18] BOULEIMEN K,LECOCQ H. A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version[J]. European Journal of Operational Research,2003,149(2):268-281.
[19] YANG H,YANG Y,YANG Z,et al. A particle swarm optimization algorithm based on simulated annealing[J]. Advanced Materials Research,2014(989-994):2301-2305.
[20] PELUSI L,PASSARELLA A,CONTI M. Opportunistic networking:data forwarding in disconnected mobile ad hoc networks[J]. IEEE Communications Magazine,2006,44(11):134-141.