采用弧形映射函数的二进制粒子群优化算法
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摘要
针对二进制粒子群优化算法(BPSO),采用S形映射函数,将粒子在空间中飞行速度的正负值大小映射为其位置向量取1的概率,易于陷入局部最优解的问题,本文提出了采用弧形映射函数的二进制粒子群优化算法(ABPSO).该算法采用弧形映射函数取代BPSO中的S形映射函数,将速度平方值大小映射为位置向量改变的概率大小,当粒子具有较低的速度平方值时能够维持在原位置,较高的速度平方值时改变位置,从而使算法更好地收敛于全局最优解;同时,采用了无强制性位置更新程序,符合弧形映射函数使用速度平方大小映射为位置改变概率大小的需要.通过六个基准函数的仿真实验发现,ABPSO具有更好的收敛精度和更高的收敛速度;ABPSO采用更加符合粒子运动规律的弧形映射函数,表现出更好的收敛于全局最优解的能力和更高的收敛速度.
Aiming at the problem that binary particle swarm optimization algorithm( BPSO),using S shaped transfer function transfers particle's flight speed in space as the probability being one of the position vector,easily fall into the local optimal,this paper proposes a binary particle swarm optimization algorithm with arc shaped transfer function( ABPSO). The algorithm uses arc shaped transfer function to replace S shaped transfer function in BPSO,transfering the square value of the velocity as the probability of changing the position vector,so that particles in a lower rate of square value can be maintained in the original position,the higher the value of the square change position,which makes the algorithm better convergence to the global optimal solution; Through the simulation experiment of six benchmark functions showed that ABPSO has better convergence accuracy and higher convergence speed; ABPSO using arc shaped transfer function which is more consistent with the particle motion,shows better ability to converge to the global optimal solution and higher convergence speed.
Aiming at the problem that binary particle swarm optimization algorithm( BPSO),using S shaped transfer function transfers particle's flight speed in space as the probability being one of the position vector,easily fall into the local optimal,this paper proposes a binary particle swarm optimization algorithm with arc shaped transfer function( ABPSO). The algorithm uses arc shaped transfer function to replace S shaped transfer function in BPSO,transfering the square value of the velocity as the probability of changing the position vector,so that particles in a lower rate of square value can be maintained in the original position,the higher the value of the square change position,which makes the algorithm better convergence to the global optimal solution; Through the simulation experiment of six benchmark functions showed that ABPSO has better convergence accuracy and higher convergence speed; ABPSO using arc shaped transfer function which is more consistent with the particle motion,shows better ability to converge to the global optimal solution and higher convergence speed.
引文
[1]Eberhart R C,Kennedy J.A newoptimizer using particle swarm theory[C].Proceedings of the Sixth International Symposium on Micro Machine and Human Science(MHS),Nagoya,Japan,1995:39-43.
[2]Sun Hui,Zhu De-gang,Wang Hui,et al.Particle swarm optimization algorithm for multi-species subspace learning[J].Journal of Chinese Computer Systems,2016,37(9):2054-2059.
[3]Chen Ying,Guo Wen-zhong,Chen Guo-long,et al.A dynamic task allocation algorithm with parallel coalition in wireless sensor networks[J].Journal of Chinese Computer Systems,2012,33(3):480-485.
[4]Geem Z W,Kim J H,Loganathan G V.A newheuristic optimization algorithm:harmony search[J].Simulation,2001,76(2):60-68.
[5]Storn R,Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[6]Tayarani-N MH,Akbarzadeh-T MR.Magnetic optimization algorithms a newsynthesis[C].Proceedings of 2008 IEEE Congress on Evolutionary Computation(CEC),Hong Kong,China,2008:2659-2664.
[7]Rashedi E,Nezamabadi-pour H,Saryazdi S.GSA:a gravitational search algorithm[J].Information Sciences,2009,179(13):2232-2248.
[8]Wang L,Xu Y,Mao Y,et al.A discrete harmony search algorithm[M].Life System Modeling and Intelligent Computing,Springer Berlin Heidelberg,2010.
[9]Wang L,Fu X,Menhas MI,et al.A modified binary differential evolution algorithm[M].Life System Modeling and Intelligent Computing,Springer Berlin Heidelberg,2010.
[10]Mirjalili S A,Hashim S Z M.BMOA:binary magnetic optimization algorithm[J].International Journal of Machine Learning and Computing,2012,2(3):204-208.
[11]Rashedi E,Nezamabadi-pour H,Saryazdi S.BGSA:binary gravitational search algorithm[J].Natural Computing,2010,9(3):727-745.
[12]Kennedy J,Eberhart R C.A discrete binary version of the particle swarm algorithm[C].Proceedings of 1997 IEEE International Conference on Systems,Man,and Cybernetics(ICSMC),Orlando,USA:1997:4104-4108.
[13]Luh G C,Lin C Y,Lin Y S.A binary particle swarm optimization for continuum structural topology optimization[J].Applied Soft Computing,2011,11(2):2833-2844.
[14]Yin P Y.A discrete particle swarm algorithm for optimal polygonal approximation of digital curves[J].Journal of Visual Communication and Image Representation,2004,15(2):241-260.
[15]Wang L,Wang X,Fu J,et al.A novel probability binary particle swarm optimization algorithm and its application[J].Journal of Software,2008,3(9):28-35.
[16]Chuang L Y,Tsai S W,Yang C H.Improved binary particle swarm optimization using catfish effect for feature selection[J].Expert Systems with Applications,2011,38(10):12699-12707.
[17]Mirjalili S,Lewis A.S-shaped versus v-shaped transfer functions for binary particle swarm optimization[J].Swarm and Evolutionary Computation,2013,9(4):1-14.
[18]Liu J,Mei Y,Li X.An analysis of the inertia weight parameter for binary particle swarm optimization[J].IEEE Transactions on Evolutionary Computation(TEVC),2016,20(5):666-681.
[2]孙辉,朱德刚,王晖,等.多种群子空间学习粒子群优化算法[J].小型微型计算机系统,2016,37(9):2054-2059.
[3]陈颖,郭文忠,陈国龙,等.无线传感器网络中带并行联盟的动态任务分配算法[J].小型微型计算机系统,2012,33(3):480-485.
[2]Sun Hui,Zhu De-gang,Wang Hui,et al.Particle swarm optimization algorithm for multi-species subspace learning[J].Journal of Chinese Computer Systems,2016,37(9):2054-2059.
[3]Chen Ying,Guo Wen-zhong,Chen Guo-long,et al.A dynamic task allocation algorithm with parallel coalition in wireless sensor networks[J].Journal of Chinese Computer Systems,2012,33(3):480-485.
[4]Geem Z W,Kim J H,Loganathan G V.A newheuristic optimization algorithm:harmony search[J].Simulation,2001,76(2):60-68.
[5]Storn R,Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[6]Tayarani-N MH,Akbarzadeh-T MR.Magnetic optimization algorithms a newsynthesis[C].Proceedings of 2008 IEEE Congress on Evolutionary Computation(CEC),Hong Kong,China,2008:2659-2664.
[7]Rashedi E,Nezamabadi-pour H,Saryazdi S.GSA:a gravitational search algorithm[J].Information Sciences,2009,179(13):2232-2248.
[8]Wang L,Xu Y,Mao Y,et al.A discrete harmony search algorithm[M].Life System Modeling and Intelligent Computing,Springer Berlin Heidelberg,2010.
[9]Wang L,Fu X,Menhas MI,et al.A modified binary differential evolution algorithm[M].Life System Modeling and Intelligent Computing,Springer Berlin Heidelberg,2010.
[10]Mirjalili S A,Hashim S Z M.BMOA:binary magnetic optimization algorithm[J].International Journal of Machine Learning and Computing,2012,2(3):204-208.
[11]Rashedi E,Nezamabadi-pour H,Saryazdi S.BGSA:binary gravitational search algorithm[J].Natural Computing,2010,9(3):727-745.
[12]Kennedy J,Eberhart R C.A discrete binary version of the particle swarm algorithm[C].Proceedings of 1997 IEEE International Conference on Systems,Man,and Cybernetics(ICSMC),Orlando,USA:1997:4104-4108.
[13]Luh G C,Lin C Y,Lin Y S.A binary particle swarm optimization for continuum structural topology optimization[J].Applied Soft Computing,2011,11(2):2833-2844.
[14]Yin P Y.A discrete particle swarm algorithm for optimal polygonal approximation of digital curves[J].Journal of Visual Communication and Image Representation,2004,15(2):241-260.
[15]Wang L,Wang X,Fu J,et al.A novel probability binary particle swarm optimization algorithm and its application[J].Journal of Software,2008,3(9):28-35.
[16]Chuang L Y,Tsai S W,Yang C H.Improved binary particle swarm optimization using catfish effect for feature selection[J].Expert Systems with Applications,2011,38(10):12699-12707.
[17]Mirjalili S,Lewis A.S-shaped versus v-shaped transfer functions for binary particle swarm optimization[J].Swarm and Evolutionary Computation,2013,9(4):1-14.
[18]Liu J,Mei Y,Li X.An analysis of the inertia weight parameter for binary particle swarm optimization[J].IEEE Transactions on Evolutionary Computation(TEVC),2016,20(5):666-681.
[2]孙辉,朱德刚,王晖,等.多种群子空间学习粒子群优化算法[J].小型微型计算机系统,2016,37(9):2054-2059.
[3]陈颖,郭文忠,陈国龙,等.无线传感器网络中带并行联盟的动态任务分配算法[J].小型微型计算机系统,2012,33(3):480-485.