一种基于改进QPSO的机器人路径规划算法
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摘要
针对量子行为粒子群优化(QPSO)算法在移动机器人路径规划中出现早熟收敛的问题,提出一种基于聚集度因子和阶段变异策略的改进QPSO算法。根据目标函数计算粒子的适应度值,在压缩扩张因子中引入改进聚集度因子划分搜索阶段,利用分阶段变异策略更新个体位置,并对算法进行性能测试。实验结果表明,与FE-PSO算法相比,该算法具有较高的收敛精度与较好的稳定性。
Aiming at the problem of premature convergence in the path planning of mobile robot based on Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,an improved QPSO algorithm based on aggregation factor and phase mutation strategy is proposed.According to the objective function,the fitness value of the particle is calculated.By introducing the improved aggregation degree factor into the compression expansion factor,the search phase is divided,the individual position is updated by the phased mutation strategy,and the performance of the algorithm is tested.Experimental results show that compared with the FE-PSO algorithm,this algorithm has higher convergence accuracy and better stability.
Aiming at the problem of premature convergence in the path planning of mobile robot based on Quantum-behaved Particle Swarm Optimization(QPSO) algorithm,an improved QPSO algorithm based on aggregation factor and phase mutation strategy is proposed.According to the objective function,the fitness value of the particle is calculated.By introducing the improved aggregation degree factor into the compression expansion factor,the search phase is divided,the individual position is updated by the phased mutation strategy,and the performance of the algorithm is tested.Experimental results show that compared with the FE-PSO algorithm,this algorithm has higher convergence accuracy and better stability.
引文
[1] KENNEDY J,EBERTHART R.Particle swarm optimiza-tion[C]//Proceedings of IEEE International Conference on Neural Networks.Washington D.C.,USA:IEEE Press,1995:1942-1948.
[2] DAS P K,BEHERA H S,DAS S,et al.A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment[J].Neurocomputing,2016,207(C):735-753.
[3] SONG B,WANG Z,ZOU L.On global smooth path planning for mobile robots using a novel multimodal dlayed PSO algorithm[J].Cognitive Computation,2017,9(1):5-17.
[4] BERGH F V D.An analysis of particle swarm optimizers[D].Pretoria,South Africa:University of Pretoria,2002.
[5] SUN J,XU W,FENG B.A global search strategy of quatntum-behaved particles swarm optimization[C]//Proceedings of IEEE Conference on Cybernetics and Intelligent Systems.Washington D.C.,USA:IEEE Press,2004:111-116.
[6] TAN W,DONG S,LI X,et al.An improved PSO algorithm based on SA and quantum theory and its application[J].International Journal of Database Theory and Application,2015,8(6):189-198.
[7] XUE T,LI R,TOKGO M,et al.Trajectory planning for autonomous mobile robot using a hybrid improved QPSO algorithm[J].Soft Computing,2017,21(9):2421-2437.
[8] 伍永健,陈跃动,陈孟元.改进QPSO和Morphin算法下移动机器人混合路径规划[J].电子测量与仪器学报,2017,31(2):295-301.
[9] 郭蕴华,王晓宗.基于改进量子粒子群算法的无人机路径规划[J].航海工程,2016,45(1):99-102.
[10] FU Y G,DING M Y,ZHOU C P.Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV[J].IEEE Transactions on Systems Man and Cybernetics,2012,42(2):511-526.
[11] SUN J,FANG W,PALADE V,et al.Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point[J].Applied Mathematics and Computation,2011,218(7):3763-3775.
[12] 吴涛,严余松,陈曦.基于随机评价机制的双子群QPSO算法[J].控制与决策,2015,30(3):526-530.
[13] STORN R.Differential evolution design of an IIR-filter[C]//Proceedings of IEEE International Conference on Evolutionary Computation.Washington D.C.,USA:IEEE Press,1996:268-273.
[14] 孙波.基于改进量子粒子群的视觉跟踪方法[D].南京:南京邮电大学,2016.
[15] 黄泽霞,俞攸红,黄德才.惯性权自适应调整的量子粒子群优化算法[J].上海交通大学学报(自然科学版),2012,46(2):228-232.
[16] AMOSHAHY M J,SHAMSI M,SEDAAGHI M H.A novel flexible inertia weight particle swarm optimization algorithm[J].Plos One,2016,11(8).
[17] TOKGO M,LI R F.Estimation method for path planning parameter based on a modified QPSO algorithm[C]//Proceedings of International Conference on Artificial Intelligence:Methodology,Systems,and Applications.Berlin,Germany:Springer,2014:261-269.
[2] DAS P K,BEHERA H S,DAS S,et al.A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment[J].Neurocomputing,2016,207(C):735-753.
[3] SONG B,WANG Z,ZOU L.On global smooth path planning for mobile robots using a novel multimodal dlayed PSO algorithm[J].Cognitive Computation,2017,9(1):5-17.
[4] BERGH F V D.An analysis of particle swarm optimizers[D].Pretoria,South Africa:University of Pretoria,2002.
[5] SUN J,XU W,FENG B.A global search strategy of quatntum-behaved particles swarm optimization[C]//Proceedings of IEEE Conference on Cybernetics and Intelligent Systems.Washington D.C.,USA:IEEE Press,2004:111-116.
[6] TAN W,DONG S,LI X,et al.An improved PSO algorithm based on SA and quantum theory and its application[J].International Journal of Database Theory and Application,2015,8(6):189-198.
[7] XUE T,LI R,TOKGO M,et al.Trajectory planning for autonomous mobile robot using a hybrid improved QPSO algorithm[J].Soft Computing,2017,21(9):2421-2437.
[8] 伍永健,陈跃动,陈孟元.改进QPSO和Morphin算法下移动机器人混合路径规划[J].电子测量与仪器学报,2017,31(2):295-301.
[9] 郭蕴华,王晓宗.基于改进量子粒子群算法的无人机路径规划[J].航海工程,2016,45(1):99-102.
[10] FU Y G,DING M Y,ZHOU C P.Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV[J].IEEE Transactions on Systems Man and Cybernetics,2012,42(2):511-526.
[11] SUN J,FANG W,PALADE V,et al.Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point[J].Applied Mathematics and Computation,2011,218(7):3763-3775.
[12] 吴涛,严余松,陈曦.基于随机评价机制的双子群QPSO算法[J].控制与决策,2015,30(3):526-530.
[13] STORN R.Differential evolution design of an IIR-filter[C]//Proceedings of IEEE International Conference on Evolutionary Computation.Washington D.C.,USA:IEEE Press,1996:268-273.
[14] 孙波.基于改进量子粒子群的视觉跟踪方法[D].南京:南京邮电大学,2016.
[15] 黄泽霞,俞攸红,黄德才.惯性权自适应调整的量子粒子群优化算法[J].上海交通大学学报(自然科学版),2012,46(2):228-232.
[16] AMOSHAHY M J,SHAMSI M,SEDAAGHI M H.A novel flexible inertia weight particle swarm optimization algorithm[J].Plos One,2016,11(8).
[17] TOKGO M,LI R F.Estimation method for path planning parameter based on a modified QPSO algorithm[C]//Proceedings of International Conference on Artificial Intelligence:Methodology,Systems,and Applications.Berlin,Germany:Springer,2014:261-269.