基于信息熵的改进海豚群算法及其桁架优化
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摘要
针对基本海豚群算法易陷入局部最优的缺陷,提出了基于信息熵的改进海豚群算法,引入信息熵来度量海豚群搜索阶段的不确定性,控制搜索阶段的选择概率,降低盲目搜索,克服了基本海豚群算法搜索阶段易陷入局部最优和早熟收敛的缺陷。将改进后的算法应用到桁架结构的优化中,并与其他算法优化结果进行了比较,证明了改进的算法在收敛速度和寻优精度方面有更好的表现,将其应用到桁架结构优化设计中,为结构优化设计提供了一种有效的方法。
Aiming at the disadvantage that basic dolphin swarm algorithm is easy to fall into local optimum,an improved dolphin swarm algorithm based on information entropy is proposed.The algorithm measures the uncertainty of dolphin swarm search phase by information entropy,controls the selection probability of search phase,reduces blind search,and overcomes the shortcomings of local optimum and premature convergence in the search phase of basic dolphin swarm algorithm.The improved algorithm is applied to the optimization of truss structure and compared with other algorithms.It is proved that the improved algorithm has better performance in convergence speed and optimization accuracy.It is applied to optimum design of truss structure,and provides an effective method for optimum design of structure.
Aiming at the disadvantage that basic dolphin swarm algorithm is easy to fall into local optimum,an improved dolphin swarm algorithm based on information entropy is proposed.The algorithm measures the uncertainty of dolphin swarm search phase by information entropy,controls the selection probability of search phase,reduces blind search,and overcomes the shortcomings of local optimum and premature convergence in the search phase of basic dolphin swarm algorithm.The improved algorithm is applied to the optimization of truss structure and compared with other algorithms.It is proved that the improved algorithm has better performance in convergence speed and optimization accuracy.It is applied to optimum design of truss structure,and provides an effective method for optimum design of structure.
引文
[1]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by a colony of cooperating agents[J].IEEE Transactionson Systems,Man andCybernetics,Part B(Cybernetics),1996,26(1):29-41.
[2]Deepa O,Senthilkumar A.Swarm intelligence from natural to artificial systems:ant colony optimization[J].International Journal on Applications of Graph Theory in Wireless Ad Hoc Networks and Sensor Networks,2016,8(1):9-17.
[3]Reid C R,Latty T.Collective behaviour and swarm intelligence in slime moulds[J].FEMS Microbiology Reviews,2016,40(6):798-806.
[4]张金梦,刘慧君.遗传算法优化BP神经网络的泊车位数量预测[J].重庆大学学报,2018,41(3):76-81.ZHANG Jinmeng,LIU Huijun.Prediction of parking space based on BP neural network optimized by genetic algorithm[J].Journal of Chongqing University,2018,41(3):76-81.(in Chinese)
[5]Strasser S,Goodman R,Sheppard J.A new discrete particle swarm optimization algorithm[C].Genetic and Evolutionary Computation Conference,Denver,CO,2016:53-60.
[6]Rahmat-Samii Y,Gies D,Robinson J.Particle swarm optimization(PSO):A novel paradigm for antenna designs[J].URSIRadio Science Bulletin,2003(306):14-22.
[7]Wang G G,Gandomi A H,Alavi A H,et al.A hybrid method based on krill herd and quantum-behaved particle swarm optimization[J].Neural Computing&Applications,2016,27(4):989-1006.
[8]Renaudineau H,Donatantonio F,Fontchastagner J,et al.A PSO-based global MPPT technique for distributed PVpower generation[J].IEEE Transactions on Industrial Electronics,2015,62(2):1047-1058.
[9]Karaboga D.An idea based on honey bee swarm for numerical optimization[R].Technical Report-TR06,Erciyes University,Turkey,2005.
[10]Akay B,Karaboga D.A modified Artificial Bee Colony algorithm for real-parameter optimization[J].Information Sciences,2012,192:120-142.
[11]郭一君,周杰,王时龙,等.基于改进人工蜂群算法和极限学习机的刀具磨损监测[J].重庆大学学报,2018,41(6):1-8.GUO Yijun,ZHOU Jie,WANG Shilong,et al.Tool wearmonitoring based on improved artificial bee colony algorithm and extreme learning machine[J].Journal of Chongqing University(Natural Science Edition),2018,41(6):1-8.(in Chinese)
[12]Mavrovouniotis M,Li C,Yang S.A survey of swarm intelligence for dynamic optimization:algorithms and applications[J].Swarm&Evolutionary Computation,2017,33:1-17.
[13]Wu T Q,Yao M,Yang J H.Dolphin swarm algorithm[J].Frontiers of Information Technology&Electronic Engineering,2016,17(8):717-729.
[14]李卫忠,李志鹏,江洋,等.混沌海豚群优化灰色神经网络的空中目标威胁评估[J].控制与决策,2018,33(11):1997-2003.LI Weizhong,LI Zhipeng,JIANG Yang,et al.Air-targets threat assessment using grey neural network optimized by chaotic dolphin swarm algorithm[J].Control and Decision,2018,33(11):1997-2003.(in Chinese)
[15]Kaveh A,Farhoudi N.A new optimization method:dolphin echolocation[J].Advances in Engineering Software,2013,59(5):53-70.
[16]Wang Y,Wang T,Zhang C Z,et al.A new stochastic optimization approach:dolphin swarm optimization algorithm[J].International Journal of Computational Intelligence and Applications,2016,15(2):1650011.
[17]Shannon C E.Prediction and entropy of printed English[J].Bell System Technical Journal,1951,30(1):50-64.
[18]Arieh Ben-Naim.Entropy,Shannon’s measure of information and boltzmann’sh-theorem[J].Entropy,2017,19(2):48.
[19]Nemzer L R.Shannon information entropy in the canonical genetic code[J].Journal of Theoretical Biology,2017,415:158-170.
[20]Wang H,Yao X.Objective reduction based on nonlinear correlation information entropy[J].Soft Computing,2016,20(6):2393-2407.
[21]Ye G,Pan C,Huang X,et al.A chaotic image encryption algorithm based on information entropy[J].International Journal of Bifurcation&Chaos,2018,28(1):1850010.
[22]李彦苍,彭扬.基于信息熵的改进人工蜂群算法[J].控制与决策,2015,30(6):1121-1125.LI Yancang,PENG Yang.Improved artificial bee colony algorithm based on information entropy[J].Control and Decision,2015,30(6):1121-1125.(in Chinese)
[2]Deepa O,Senthilkumar A.Swarm intelligence from natural to artificial systems:ant colony optimization[J].International Journal on Applications of Graph Theory in Wireless Ad Hoc Networks and Sensor Networks,2016,8(1):9-17.
[3]Reid C R,Latty T.Collective behaviour and swarm intelligence in slime moulds[J].FEMS Microbiology Reviews,2016,40(6):798-806.
[4]张金梦,刘慧君.遗传算法优化BP神经网络的泊车位数量预测[J].重庆大学学报,2018,41(3):76-81.ZHANG Jinmeng,LIU Huijun.Prediction of parking space based on BP neural network optimized by genetic algorithm[J].Journal of Chongqing University,2018,41(3):76-81.(in Chinese)
[5]Strasser S,Goodman R,Sheppard J.A new discrete particle swarm optimization algorithm[C].Genetic and Evolutionary Computation Conference,Denver,CO,2016:53-60.
[6]Rahmat-Samii Y,Gies D,Robinson J.Particle swarm optimization(PSO):A novel paradigm for antenna designs[J].URSIRadio Science Bulletin,2003(306):14-22.
[7]Wang G G,Gandomi A H,Alavi A H,et al.A hybrid method based on krill herd and quantum-behaved particle swarm optimization[J].Neural Computing&Applications,2016,27(4):989-1006.
[8]Renaudineau H,Donatantonio F,Fontchastagner J,et al.A PSO-based global MPPT technique for distributed PVpower generation[J].IEEE Transactions on Industrial Electronics,2015,62(2):1047-1058.
[9]Karaboga D.An idea based on honey bee swarm for numerical optimization[R].Technical Report-TR06,Erciyes University,Turkey,2005.
[10]Akay B,Karaboga D.A modified Artificial Bee Colony algorithm for real-parameter optimization[J].Information Sciences,2012,192:120-142.
[11]郭一君,周杰,王时龙,等.基于改进人工蜂群算法和极限学习机的刀具磨损监测[J].重庆大学学报,2018,41(6):1-8.GUO Yijun,ZHOU Jie,WANG Shilong,et al.Tool wearmonitoring based on improved artificial bee colony algorithm and extreme learning machine[J].Journal of Chongqing University(Natural Science Edition),2018,41(6):1-8.(in Chinese)
[12]Mavrovouniotis M,Li C,Yang S.A survey of swarm intelligence for dynamic optimization:algorithms and applications[J].Swarm&Evolutionary Computation,2017,33:1-17.
[13]Wu T Q,Yao M,Yang J H.Dolphin swarm algorithm[J].Frontiers of Information Technology&Electronic Engineering,2016,17(8):717-729.
[14]李卫忠,李志鹏,江洋,等.混沌海豚群优化灰色神经网络的空中目标威胁评估[J].控制与决策,2018,33(11):1997-2003.LI Weizhong,LI Zhipeng,JIANG Yang,et al.Air-targets threat assessment using grey neural network optimized by chaotic dolphin swarm algorithm[J].Control and Decision,2018,33(11):1997-2003.(in Chinese)
[15]Kaveh A,Farhoudi N.A new optimization method:dolphin echolocation[J].Advances in Engineering Software,2013,59(5):53-70.
[16]Wang Y,Wang T,Zhang C Z,et al.A new stochastic optimization approach:dolphin swarm optimization algorithm[J].International Journal of Computational Intelligence and Applications,2016,15(2):1650011.
[17]Shannon C E.Prediction and entropy of printed English[J].Bell System Technical Journal,1951,30(1):50-64.
[18]Arieh Ben-Naim.Entropy,Shannon’s measure of information and boltzmann’sh-theorem[J].Entropy,2017,19(2):48.
[19]Nemzer L R.Shannon information entropy in the canonical genetic code[J].Journal of Theoretical Biology,2017,415:158-170.
[20]Wang H,Yao X.Objective reduction based on nonlinear correlation information entropy[J].Soft Computing,2016,20(6):2393-2407.
[21]Ye G,Pan C,Huang X,et al.A chaotic image encryption algorithm based on information entropy[J].International Journal of Bifurcation&Chaos,2018,28(1):1850010.
[22]李彦苍,彭扬.基于信息熵的改进人工蜂群算法[J].控制与决策,2015,30(6):1121-1125.LI Yancang,PENG Yang.Improved artificial bee colony algorithm based on information entropy[J].Control and Decision,2015,30(6):1121-1125.(in Chinese)