基于改进PSO算法的机动通信保障任务分配方法
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摘要
针对机动通信保障问题建立任务分配模型,结合梯度下降法提出一种基于改进粒子群算法(TSPSO)的任务分配模型求解方法.在TSPSO算法中增加判断极值陷阱、粒子二次搜索、设定禁忌区域、粒子淘汰与生成4个部分,并将TSPSO算法与其他4种改进PSO算法应用于四种典型测试函数的优化.结果表明,TSPSO算法收敛精度更高、收敛速度更快.在基于TSPSO算法的任务分配模型求解方法中,基于各机动通信保障单元到不同通信地点分配概率的思想对粒子群进行编码和解码,提高模型求解效率.仿真结果表明,TSPSO算法能够快速寻找到机动通信保障任务最优分配方案.
For solving the problem of tactical communication support, a task allocation model is established, and a twice search particle swarm optimization(TSPSO) algorithm based on gradient descent is proposed. There are four parts added into the TSPSO algorithm, including determination of the extremum trap, particle twices search, set forbidden area, particle elimination and generated. The TSPSO algorithm and other four improved algorithms are applied to the optimization problem of four typical test functions. The results show that, the convergence accuracy of the TSPSO algorithm is higher, the convergence speed is faster. In the solution of the task allocation model, the probability distribution of the support units to communication places is used to encode and decode the particle swarm. The simulation results show that the proposed algorithm can quickly find the optimal allocation scheme of tactical communication support tasks.
For solving the problem of tactical communication support, a task allocation model is established, and a twice search particle swarm optimization(TSPSO) algorithm based on gradient descent is proposed. There are four parts added into the TSPSO algorithm, including determination of the extremum trap, particle twices search, set forbidden area, particle elimination and generated. The TSPSO algorithm and other four improved algorithms are applied to the optimization problem of four typical test functions. The results show that, the convergence accuracy of the TSPSO algorithm is higher, the convergence speed is faster. In the solution of the task allocation model, the probability distribution of the support units to communication places is used to encode and decode the particle swarm. The simulation results show that the proposed algorithm can quickly find the optimal allocation scheme of tactical communication support tasks.
引文
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[2]李宏,焦永昌,张莉.一种求解混合整数规划的混合进化算法[J].控制与决策,2008,23(10):1098-1102.(Li H,Jiao Y C,Zhang L.Hybrid evolutionary algorithm for mixed-integer programming problems[J].Control and Decision,2008,23(10):1098-1102.)
[3]王虹,卫军胡,刘昌军,等.引入群体发现和加入行为的随机搜索算法[J].西安交通大学学报,2013,47(12):43-49.(Wang H,Wei J H,Liu C J,et al.A novel adaptive stochastic search algorithm based on group founding andjoining behaviors[J].J of Xi’an Jiaotong University,2013,47(12):43-49.)
[4]Jia L P,Wang Y P,Fan L.An improved uniform design-based genetic algorithm for multi-objective bilevel convex programming[J].Int J Computation Science and Engineering,2016,12(1):38-46.
[5]Ben M,Patricia R,Cameron S,et al.Evaluating the seeding genetic algorithm[J].Lecture Notes in Computer Science,2013,8272(1):228-233.
[6]方璐,詹军,徐先勇,等.基于多智能体遗传算法的配电网节能降耗综合管理系统[J].湖南大学学报:自然科学版,2016,43(4):105-112.(Fang L,Zhan J,Xu X Y,et al.Energy saving management system of distribution network based on multi-agent genetic algorithm[J].J of Hu’nan University:Natural Science,2016,43(4):105-112.)
[7]Liao T J,Thomas S.A unied ant colony optimization algorithm for continuous optimization[J].European J of Operational Research,2014,234(3):597-609.
[8]马荣贵,崔华,薛世焦,等.改进蚁群算法的多约束质量最优路径选择[J].西安电子科技大学学报:自然科学版,2016,43(3):185-189.(Ma R G,Cui H,Xue S J,et al.Improved ant colony algorithm for the optimal-quality-path routing problem with multi-constraints[J].J of Xidian University:Natural Science,2016,43(3):185-189.)
[9]王生生,杨娟娟,柴胜.基于混沌鲶鱼效应的人工蜂群算法及应用[J].电子学报,2014,42(9):1731-1737.(Wang S S,Yang J J,Chai S.Artificial bee colony algorithm with chaotic catfish effect and its application[J].Acta Electronica Sinica,2014,42(9):1731-1737.)
[10]Kennedy J,Eberhart R.Particle swarm optimization[C].Proc of the 4th Int Conf on Neural Networks.Piscataway:IEEE,1995:1942-1948.
[11]罗磊,陈恳,杜峰坡,等.基于改进型粒子群算法的曲面匹配与位姿获取[J].清华大学学报:自然科学版,2015,55(10):1061-1066.(Luo L,Chen K,Du F P,et al.Surface fitting and position-pose measurements based on an improved SA-PSO algorithm[J].J of Tsinghua University:Science and Technology,2015,55(10):1061-1066.)
[12]严浙平,邓超,赵玉飞,等.改进粒子群算法在UUV航迹规划中的应用[J].华中科技大学学报:自然科学版,2013,41(12):64-68.(Yan Z P,Deng C,Zhao Y F,et al.Application of improved particle swarm algorithm to UUV 3-D path planning[J].J of Huazhong University of Science and Technology:Natural Science Edition.2013,41(12):64-68.)
[13]许少华,宋美玲,许辰,等.一种基于混合误差梯度下降算法的过程神经网络训练[J].东北石油大学学报,2014,38(4):92-96.(Xu S H,Song M L,Xu C,et al.Training algorithm of process neural networks based on hybrid error gradient descent[J].J of Northeast Petroleum University,2014,38(4):92-96.)
[14]汤可宗,李慧颖,李娟,等.一种求解复杂优化问题的改进粒子群优化算法[J].南京理工大学学报,2015,39(4):386-391.(Tang K Z,Li H Y,Li J,et al.Improved particle swarm optimization algorithm for solving complex optimization problems[J].J of Nanjing University of Science and Technology,2015,39(4):386-391.)
[15]Riget R,Vesterstrom J S.A diversity-guided particle swarm optimizer-he ARPSO[R].Denmark:University of Aarhus,2002.
[16]汤可宗.遗传算法与粒子群优化算法的改进及应用研究[D].南京:南京理工大学计算机科学与工程学院,2012.(Tang K Z.Improvement and application of genetic algorithm and particle swarm algorithm research[D].Nanjing:Institute of Computer Science and Eengineering,Nanjing University of Technology,2012.)
[17]周利军,彭卫,曾小强,等.基于杂交变异的动态粒子群优化算法[J].计算机科学,2013,40(11A):143-146.(Zhou L J,Peng W,Zeng X Q,et al.Dynamic particle swarm optimization based on hybrid variable[J].Computer Science,2013,40(11A):143-146.)
[18]白俊强,尹戈玲,孙智伟,等.基于二阶振荡及自然选择的随机权重混合粒子群算法[J].控制与决策,2012,27(10):1459-1465.(Bai J Q,Yin G L,Sun Z W,et al.Random weighted hybrid particle swarm optimization algorithm based on second order oscillation and natural selection[J].Control and Decision,2012,27(10):1459-1465.)
[19]Wei J X,Sun Y H,Su X N.A novel particle swarm optimization based on immune selection[J].J of Nanjing University:Natural Sciences,2010,46(1):1-8.
[20]Zhan Z H,Zhang J,Li Y,et al.Adaptive particle swarm optimization[J].IEEE Trans on Systems,Man,and Cybernetics,Part B:Cybernetics,2009,39(6):1362-1381.