基于微粒群优化算法的无线市话系统基站分布规划的研究
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摘要
SCDMA(Synchronous Code Division Multiple Access--同步码分多址)接入技术采用了智能天线、软件无线电等先进技术,是一个全新的我国拥有完整自主知识产权的无线通信技术标准。SCDMA系统中基站分布规划的研究对于后期网络建设和系统优化有着重要的理论意义和实用价值。
SCDMA系统基站分布规划属于多目标组合优化问题,需要在满足目标区域的最小场强达到通信要求下,还必须满足业务质量要求,业务覆盖要求,以及降低经济成本。然而这些目标之间往往存在着一定的冲突矛盾。因此在求解过程中,要设法平衡各个目标或找到解决冲突的方法。本文采用了子微粒群的概念,加强了共享信息,降低了计算代价。实验证明该方法更加灵活有效的捕捉到了最优解。
微粒群优化算法的诸多优势适于解决多目标优化问题。但是种群的多样性随着时间的增加而下降过快,容易陷入局部最优解。为了降低在初期算法陷入局部最优解的可能性,本文融合了遗传算法中的轮盘赌选择算子。实验证明该方法能在早期抑制微粒的“早熟”,得到较快的收敛速度。
当算法即将收敛时,种群中的个体大部分都集中在局部最优解或全局最优解附近,即发生早熟现象。为了使个体跳出该局限区域,本文应用了爬坡算子,加强其爬坡能力,加快其跳出局部最优解的能力。实验证明爬坡算子的引入在后期最大程度上避免了“早熟”的发生。
SCDMA(Synchronous Code Division Multiple Access) technology is a completely new technical standard for wireless communications.Because it uses smart antenna,software radio and other advanced technology With independent intellectual property rights.It attaches theoretical and practical value to post-network construction and system optimization by the research of SCDMA base stations'placement.
The aim of SCDMA base stations'placement is not only to meet minimum field strength in the target region,but also to meet the quality requirements,business coverage,as well as the reduction of the economic cost.However,these objectives are often conflictive.Thus in the solving process,we should try to balance the various objectives or to find a solution to them.By the concept of sub-particle swarm,it enhances information sharing and reduces the computational cost.Experiment shows that it is more flexible and effective to capture the optimal solution.
Particle Swarm Optimization wins great advantage over multi-objective optimization problem.Nevertheless,the diversity of the population is prone to decrease with the passage of time,thus it is easy to fall into local optimal solution.In order to reduce the possibility into the local optimal solution at an early stage,the roulette selection operator often used in the genetic algorithm is introduced in this paper. Experiment shows that through this method "prematurity" is inhibited and convergence speed become faster.
When the algorithm is about to converge,most individuals are centralized in the area of local optimal solution or near global optimal solution.In other words," prematurity " has occurred.In order to escape the limitation of individual regions, climbing operator is adopted in this paper,which could strengthen its climbing ability and accelerate the possibility of moving away local optimal solution.Experiment proves that at later stage the introduction of climbing operator avoids the " prematurity" to maximum extent.
SCDMA系统基站分布规划属于多目标组合优化问题,需要在满足目标区域的最小场强达到通信要求下,还必须满足业务质量要求,业务覆盖要求,以及降低经济成本。然而这些目标之间往往存在着一定的冲突矛盾。因此在求解过程中,要设法平衡各个目标或找到解决冲突的方法。本文采用了子微粒群的概念,加强了共享信息,降低了计算代价。实验证明该方法更加灵活有效的捕捉到了最优解。
微粒群优化算法的诸多优势适于解决多目标优化问题。但是种群的多样性随着时间的增加而下降过快,容易陷入局部最优解。为了降低在初期算法陷入局部最优解的可能性,本文融合了遗传算法中的轮盘赌选择算子。实验证明该方法能在早期抑制微粒的“早熟”,得到较快的收敛速度。
当算法即将收敛时,种群中的个体大部分都集中在局部最优解或全局最优解附近,即发生早熟现象。为了使个体跳出该局限区域,本文应用了爬坡算子,加强其爬坡能力,加快其跳出局部最优解的能力。实验证明爬坡算子的引入在后期最大程度上避免了“早熟”的发生。
SCDMA(Synchronous Code Division Multiple Access) technology is a completely new technical standard for wireless communications.Because it uses smart antenna,software radio and other advanced technology With independent intellectual property rights.It attaches theoretical and practical value to post-network construction and system optimization by the research of SCDMA base stations'placement.
The aim of SCDMA base stations'placement is not only to meet minimum field strength in the target region,but also to meet the quality requirements,business coverage,as well as the reduction of the economic cost.However,these objectives are often conflictive.Thus in the solving process,we should try to balance the various objectives or to find a solution to them.By the concept of sub-particle swarm,it enhances information sharing and reduces the computational cost.Experiment shows that it is more flexible and effective to capture the optimal solution.
Particle Swarm Optimization wins great advantage over multi-objective optimization problem.Nevertheless,the diversity of the population is prone to decrease with the passage of time,thus it is easy to fall into local optimal solution.In order to reduce the possibility into the local optimal solution at an early stage,the roulette selection operator often used in the genetic algorithm is introduced in this paper. Experiment shows that through this method "prematurity" is inhibited and convergence speed become faster.
When the algorithm is about to converge,most individuals are centralized in the area of local optimal solution or near global optimal solution.In other words," prematurity " has occurred.In order to escape the limitation of individual regions, climbing operator is adopted in this paper,which could strengthen its climbing ability and accelerate the possibility of moving away local optimal solution.Experiment proves that at later stage the introduction of climbing operator avoids the " prematurity" to maximum extent.
引文
[1]王玉娜,王秋华,陈新峰.3G网络规划中自动基站分布算法的研究.无线电工程.2006.(9).
[2]张桂连,刘弘,陆佃杰.基于改进遗传算法的蜂窝网络区域配置方法.计算机工程.2008.34(12).
[3]张坤,吴跃.PSO算法在通信基站分布规划中的应用.计算机与数字工程.2006.34(4).
[4]原萍,王光兴,张洋洋.求解通信优化问题的一种微粒群优化方法.东北大学学报(自然科学版).2004.25(10).
[5]曲润涛,席裕庚,韩兵.基于遗传算法的通信网络优化规划.高技术通讯.1996.6
[6]Kennedy J,Eberhart R.C.Particle Swarm Optimization.In:Proc.IEEE Int' 1.conf.on Neural Networks,Ⅳ.Piscataway,NJ:IEEE Service Center,1995,1942-1948.
[7]曾建潮,介婧,崔志华.微粒群算法[M].北京.科学出版社.2004.
[8]杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004(6):87-94.
[9]李爱国,覃征,鲍复民等.粒子群优化算法[J].计算机工程应用.2002(3).1-3.
[10]王正志,薄涛.进化算法[M].长沙:国防科技大学出版社.2000.
[11]Eberhart R,Hu X H.Human tremor analysis using particle swarm optimization[A].Proceedings of the 1999 Congress on Evolutionary Computation[C].Piscataway,NJ,USA:IEEE,1999.1927-1930.
[12]李爱国,覃征.积单元神经网络预测噪声环境的混沌时问序列[J].西安科技学院学报.2003.23(3).295-297.
[13]Clerc M.Discrete particle optimization illustrated by the traveling salesman problem[EB/OL].http://www.mauriceclerc.net,2000.
[14]Venter G,Sobieszczanski-Sobieski J.Multidisciplinary optimization of a transport aircraft wing using particle swam optimization[J].Structure Multidiscipline Optimization.2004.26(1-2).121-131.
[15]Park J,Choi K,A]lstot D J.Parasitic-aware RF Circuit Design and Optimization[J].IEEE Transactions on Circuits and Systems In:Fundamental Theory and Applications.2004.51(10).1953-1966.
[16]Zhang X G,Yu L,Zheng Y,et al.Two-stage Adaptive PMD Compensation in a 10Gbit/s Optical Communication System Using Particle Swarm Algorithm[J].Optics Communications.2004,231.233-242.
[17]潘达儒,杜明辉.基于粒子群优化的OoS组播路由算法[J].计算机工程与应用.2006.42(1).138-140.
[18]Kaunan S,Slochanal S M R,Subbara J P,et al.Application of Particle Swarm optimization Technique and its Variants to Generation Expansion Planning Problem[J].Electric Power Systems Research.2004.70(3).203-210.
[19]Yoshida H,Kawata K,Fukuyama Y,et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment[J].IEEE Transactions on Power Systems.2000.15(4).1232-1239.
[20]Ting T O,Rao M V C,Loo C K,et al.Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization[J].Journal of Heuristics.2003.9(6).507-520.
[21]Coell C A C,et al,Use of particle swarm optimization to design combinational logic circuits[A].Proceedings of the 5th international conference on evolvable systems[C].Germany:Springer-Verlag,2003.398-409.
[22]李辉等.粒子群优化算法在FIR数字滤波器设计中的应用[J].电子学报,2005,33(7):1338-1341.
[23]杨锴,杨大成.微粒群算法在自动频率规划中的应用.中国科技论文在线.2007.1-2.
[24]Kaunan S,Slochanal S M R,Subbara J P,et al.Application of Particle Swarm optimization Technique and its Variants to Generation Expansion Planning Problem[J].Electric Power Systems Research.2004.70(3).203-210.
[25]Yoshida H,Kawata K,Fukuyama Y,et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment[J].IEEE Transactions on Power Systems.2000.15(4).1232-1239.
[26]Ting T O,Rao M V C,Loo C K,et al.Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization[J].Journal of Heuristics.2003.9(6).507-520.
[27]Coell C A C,et al,Use of particle swarm optimization to design combinational logic circuits[A].Proceedings of the 5th international conference on evolvable systems[C].Germany:Springer-Verlag,2003.398-409.
[28]朱定乾.移动通信网络规划研究与应用.东南大学.东南大学.2006,16.
[29]Shi Y,Eberhart R C.A Modified Particle Swarm Optimizer.In:Proceedings of IEEE International Conference on Evolutionary Computation.IEEE Press,Piscataway,NJ,1998.69-73.
[30]Shi Y,Eberhart R C.Fuzzy Adaptive Particle Swarm Optimization.In:Proc.Congress on Evolutionary Computation 2001.Seoul,korea,IEEE Service Center,2001.
[31]Angeline P J.Using Selection to Improve Particle Swarm Optimization.Anchorage,Alaska,USA.IEEE International Conference on Evolutionary Computation.1998.84-89.
[32]Angeline P J.Evolutionary Optimization Versus Particle Swarm Optimization:Philosophy and Performance Differences.In:The Seventh Annual Conf.on Evolutionary Programming.1998.
[33]Suganthan P N.Particle Swarm Optimiser with Neighbourhood Operator.In:Proceeding of the 1999 Congress on Evolutionary Computation.Piscataway,NJ,IEEE Service Center.1999,1958-1962.
[34]Jacques Riget and Jakob S.Vesterstr.A Diversity-guided Particle Swarm Optimizer the ARPSO.
[35]胡静,曾建潮,谭瑛.动态环境下一种改进的微粒群算法.系统工程理论与实践.2008(4).96.
[36]郑小霞,钱锋.一种改进的微粒群优化算法.计算机工程.2006.32(15).25.
[37]王芳,邱玉辉.一种引入轮盘赌选择算子的混合粒子群算法.西南师范大学学报(自然科学版).2006.31(3).
[38]刘波,王凌,金以慧,黄德先.化工自动化及仪表.2005.32(3).2-3.
[39]Pulido G T,Coello Coello C A.Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer[A].Proceedings of the Genetic and Evolutionary Computation Conference[C].Springer,2004.225-237.
[40]Mostaghim S,Teich J.Strategies for Finding Local Guides in Multi-objective Particle Swarm Optimization(MOPSO)[A].Proceedings of the IEEE Swarm Intelligence Symposium[C].2003.26-33.
[41]Ray T,Liew K M.A Swarm Metaphor for Multiobjective Design Objectives with Particle Swarm Optimization[J].Engineering Optimization,2002,34(2):141-153.
[42]Coello Coello C A,Pulido G T,Lechuga M S.Handling Multiple Objectives with Particle Swarm Optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[43]Hu X,Eberhart R.Multi-objective Optimization Using Dynamic Neighborhood Particle Swarm Optimization[A].Proceedings of the IEEE Congress on Evolutionary Computation[C].2002.1677-1681.
[44]Parsopoules K E,Vrahatis M N.Particle Swarm Optimization Method in Multiobjective Problems[A].Proceedings ACM Symposium on Applied Computing[C].2002.603-607.
[45]Li X.A Nondominated Sorting Particle Swarm Optimizer for Multiobjective Optimization[A].Proceedings of the Genetic and Evolutionary Computation Conference[C].Springer,2003.37-48.
[46]钟胜,闻英友,王朝金,王光兴.遗传算法在CDMA网络基站分布规划中的应用.邮电设计技术.2004.6.
[47]关庆学.运用改进的遗传算法解决无线市话网络基站分布规划问题.吉林大学.吉林大学.2007.42-43.
[48]龚纯,王正林.精通MATLAB最优化计算.北京.电子工业出版社.2009.
[2]张桂连,刘弘,陆佃杰.基于改进遗传算法的蜂窝网络区域配置方法.计算机工程.2008.34(12).
[3]张坤,吴跃.PSO算法在通信基站分布规划中的应用.计算机与数字工程.2006.34(4).
[4]原萍,王光兴,张洋洋.求解通信优化问题的一种微粒群优化方法.东北大学学报(自然科学版).2004.25(10).
[5]曲润涛,席裕庚,韩兵.基于遗传算法的通信网络优化规划.高技术通讯.1996.6
[6]Kennedy J,Eberhart R.C.Particle Swarm Optimization.In:Proc.IEEE Int' 1.conf.on Neural Networks,Ⅳ.Piscataway,NJ:IEEE Service Center,1995,1942-1948.
[7]曾建潮,介婧,崔志华.微粒群算法[M].北京.科学出版社.2004.
[8]杨维,李歧强.粒子群优化算法综述[J].中国工程科学,2004(6):87-94.
[9]李爱国,覃征,鲍复民等.粒子群优化算法[J].计算机工程应用.2002(3).1-3.
[10]王正志,薄涛.进化算法[M].长沙:国防科技大学出版社.2000.
[11]Eberhart R,Hu X H.Human tremor analysis using particle swarm optimization[A].Proceedings of the 1999 Congress on Evolutionary Computation[C].Piscataway,NJ,USA:IEEE,1999.1927-1930.
[12]李爱国,覃征.积单元神经网络预测噪声环境的混沌时问序列[J].西安科技学院学报.2003.23(3).295-297.
[13]Clerc M.Discrete particle optimization illustrated by the traveling salesman problem[EB/OL].http://www.mauriceclerc.net,2000.
[14]Venter G,Sobieszczanski-Sobieski J.Multidisciplinary optimization of a transport aircraft wing using particle swam optimization[J].Structure Multidiscipline Optimization.2004.26(1-2).121-131.
[15]Park J,Choi K,A]lstot D J.Parasitic-aware RF Circuit Design and Optimization[J].IEEE Transactions on Circuits and Systems In:Fundamental Theory and Applications.2004.51(10).1953-1966.
[16]Zhang X G,Yu L,Zheng Y,et al.Two-stage Adaptive PMD Compensation in a 10Gbit/s Optical Communication System Using Particle Swarm Algorithm[J].Optics Communications.2004,231.233-242.
[17]潘达儒,杜明辉.基于粒子群优化的OoS组播路由算法[J].计算机工程与应用.2006.42(1).138-140.
[18]Kaunan S,Slochanal S M R,Subbara J P,et al.Application of Particle Swarm optimization Technique and its Variants to Generation Expansion Planning Problem[J].Electric Power Systems Research.2004.70(3).203-210.
[19]Yoshida H,Kawata K,Fukuyama Y,et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment[J].IEEE Transactions on Power Systems.2000.15(4).1232-1239.
[20]Ting T O,Rao M V C,Loo C K,et al.Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization[J].Journal of Heuristics.2003.9(6).507-520.
[21]Coell C A C,et al,Use of particle swarm optimization to design combinational logic circuits[A].Proceedings of the 5th international conference on evolvable systems[C].Germany:Springer-Verlag,2003.398-409.
[22]李辉等.粒子群优化算法在FIR数字滤波器设计中的应用[J].电子学报,2005,33(7):1338-1341.
[23]杨锴,杨大成.微粒群算法在自动频率规划中的应用.中国科技论文在线.2007.1-2.
[24]Kaunan S,Slochanal S M R,Subbara J P,et al.Application of Particle Swarm optimization Technique and its Variants to Generation Expansion Planning Problem[J].Electric Power Systems Research.2004.70(3).203-210.
[25]Yoshida H,Kawata K,Fukuyama Y,et al.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment[J].IEEE Transactions on Power Systems.2000.15(4).1232-1239.
[26]Ting T O,Rao M V C,Loo C K,et al.Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization[J].Journal of Heuristics.2003.9(6).507-520.
[27]Coell C A C,et al,Use of particle swarm optimization to design combinational logic circuits[A].Proceedings of the 5th international conference on evolvable systems[C].Germany:Springer-Verlag,2003.398-409.
[28]朱定乾.移动通信网络规划研究与应用.东南大学.东南大学.2006,16.
[29]Shi Y,Eberhart R C.A Modified Particle Swarm Optimizer.In:Proceedings of IEEE International Conference on Evolutionary Computation.IEEE Press,Piscataway,NJ,1998.69-73.
[30]Shi Y,Eberhart R C.Fuzzy Adaptive Particle Swarm Optimization.In:Proc.Congress on Evolutionary Computation 2001.Seoul,korea,IEEE Service Center,2001.
[31]Angeline P J.Using Selection to Improve Particle Swarm Optimization.Anchorage,Alaska,USA.IEEE International Conference on Evolutionary Computation.1998.84-89.
[32]Angeline P J.Evolutionary Optimization Versus Particle Swarm Optimization:Philosophy and Performance Differences.In:The Seventh Annual Conf.on Evolutionary Programming.1998.
[33]Suganthan P N.Particle Swarm Optimiser with Neighbourhood Operator.In:Proceeding of the 1999 Congress on Evolutionary Computation.Piscataway,NJ,IEEE Service Center.1999,1958-1962.
[34]Jacques Riget and Jakob S.Vesterstr.A Diversity-guided Particle Swarm Optimizer the ARPSO.
[35]胡静,曾建潮,谭瑛.动态环境下一种改进的微粒群算法.系统工程理论与实践.2008(4).96.
[36]郑小霞,钱锋.一种改进的微粒群优化算法.计算机工程.2006.32(15).25.
[37]王芳,邱玉辉.一种引入轮盘赌选择算子的混合粒子群算法.西南师范大学学报(自然科学版).2006.31(3).
[38]刘波,王凌,金以慧,黄德先.化工自动化及仪表.2005.32(3).2-3.
[39]Pulido G T,Coello Coello C A.Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer[A].Proceedings of the Genetic and Evolutionary Computation Conference[C].Springer,2004.225-237.
[40]Mostaghim S,Teich J.Strategies for Finding Local Guides in Multi-objective Particle Swarm Optimization(MOPSO)[A].Proceedings of the IEEE Swarm Intelligence Symposium[C].2003.26-33.
[41]Ray T,Liew K M.A Swarm Metaphor for Multiobjective Design Objectives with Particle Swarm Optimization[J].Engineering Optimization,2002,34(2):141-153.
[42]Coello Coello C A,Pulido G T,Lechuga M S.Handling Multiple Objectives with Particle Swarm Optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[43]Hu X,Eberhart R.Multi-objective Optimization Using Dynamic Neighborhood Particle Swarm Optimization[A].Proceedings of the IEEE Congress on Evolutionary Computation[C].2002.1677-1681.
[44]Parsopoules K E,Vrahatis M N.Particle Swarm Optimization Method in Multiobjective Problems[A].Proceedings ACM Symposium on Applied Computing[C].2002.603-607.
[45]Li X.A Nondominated Sorting Particle Swarm Optimizer for Multiobjective Optimization[A].Proceedings of the Genetic and Evolutionary Computation Conference[C].Springer,2003.37-48.
[46]钟胜,闻英友,王朝金,王光兴.遗传算法在CDMA网络基站分布规划中的应用.邮电设计技术.2004.6.
[47]关庆学.运用改进的遗传算法解决无线市话网络基站分布规划问题.吉林大学.吉林大学.2007.42-43.
[48]龚纯,王正林.精通MATLAB最优化计算.北京.电子工业出版社.2009.