Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapid...Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.展开更多
The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers fro...The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.展开更多
针对蝴蝶优化算法(butterfly optimization algorithm,BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly wit...针对蝴蝶优化算法(butterfly optimization algorithm,BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning,OMSSBOA)。引入柯西变异对最优蝴蝶个体进行扰动,避免算法陷入局部最优;将改进的樽海鞘群优化算法(salp swarm algorithm,SSA)嵌入到BOA,平衡算法全局勘探和局部开采的比重,进而提高算法收敛速度;利用趋优变异反向学习策略扩大算法搜索范围并提升解的质量,进而提高算法的寻优精度。将改进算法在10种基准测试函数上进行仿真实验,结果表明,改进算法具有较好的寻优性能和鲁棒性。展开更多
为优化燃料电池混合动力系统(fuel cell hybrid power system,FCHPS)并延长其使用寿命,该文提出一种考虑电堆性能一致性的多目标优化能量管理方法。该方法的目的是降低系统等效氢耗、提高燃料电池系统内电堆组运行效率的同时限制锂电池...为优化燃料电池混合动力系统(fuel cell hybrid power system,FCHPS)并延长其使用寿命,该文提出一种考虑电堆性能一致性的多目标优化能量管理方法。该方法的目的是降低系统等效氢耗、提高燃料电池系统内电堆组运行效率的同时限制锂电池荷电状态(state of charge,SOC)波动。由于电堆组的性能会在实际运行过程中发生退化,因此该方法还考虑了电堆组的性能状态差异,通过限制性能较差电堆的运行压力,以延长系统寿命。为实现这一目的采用樽海鞘群算法(salpswarmalgorithm,SSA)对目标函数进行优化求解,得到系统最优功率分配。最后,基于RT-LAB半实物仿真平台,将所提方法与有限状态机控制方法进行对比,实验结果表明所提出的方法能够有效降低系统氢耗,提高电堆组效率的同时减缓性能较差电堆的功率波动,维持系统一致性,有利于系统长期稳定运行。展开更多
In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLS...In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.展开更多
The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning t...The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.展开更多
Identifying the parameters of photovoltaic(PV)modules is significant for their design and simulation.Because of the instabilities in the weather action and land surface of the earth,which cause errors in measuring,a n...Identifying the parameters of photovoltaic(PV)modules is significant for their design and simulation.Because of the instabilities in the weather action and land surface of the earth,which cause errors in measuring,a novel fuzzy representation-based PV module is formulated and developed.In this paper,a novel locomotion-based hybrid salp swarm algorithm(LHSSA)is presented to identify the parameters of PV modules accurately and reliably.In the LHSSA,better leader salps based on particle swarm optimization(PSO)are incorporated to the traditional salp swarm algorithm(SSA)in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA.By this integration,the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region.The proposed LHSSA is investigated on different PV models,i.e.,single-diode(SD),double-diode(DD),and PV module in crisp and fuzzy aspects.By comparing with different algorithms,the comprehensive results affirm that the LHSSA can achieve a highly competitive performance,especially on quality and reliability.展开更多
文摘Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.
基金supported by the Key R&D Program of Zhejiang(2022C03114)Zhejiang Provincial Natural Science Foundation of China(LJ19F020001,LZ22F020005)+1 种基金National Natural Science Foundation of China(62076185,U1809209)Guangdong Natural Science Foundation(2021A1515011994).
文摘The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.
文摘针对蝴蝶优化算法(butterfly optimization algorithm,BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning,OMSSBOA)。引入柯西变异对最优蝴蝶个体进行扰动,避免算法陷入局部最优;将改进的樽海鞘群优化算法(salp swarm algorithm,SSA)嵌入到BOA,平衡算法全局勘探和局部开采的比重,进而提高算法收敛速度;利用趋优变异反向学习策略扩大算法搜索范围并提升解的质量,进而提高算法的寻优精度。将改进算法在10种基准测试函数上进行仿真实验,结果表明,改进算法具有较好的寻优性能和鲁棒性。
文摘为优化燃料电池混合动力系统(fuel cell hybrid power system,FCHPS)并延长其使用寿命,该文提出一种考虑电堆性能一致性的多目标优化能量管理方法。该方法的目的是降低系统等效氢耗、提高燃料电池系统内电堆组运行效率的同时限制锂电池荷电状态(state of charge,SOC)波动。由于电堆组的性能会在实际运行过程中发生退化,因此该方法还考虑了电堆组的性能状态差异,通过限制性能较差电堆的运行压力,以延长系统寿命。为实现这一目的采用樽海鞘群算法(salpswarmalgorithm,SSA)对目标函数进行优化求解,得到系统最优功率分配。最后,基于RT-LAB半实物仿真平台,将所提方法与有限状态机控制方法进行对比,实验结果表明所提出的方法能够有效降低系统氢耗,提高电堆组效率的同时减缓性能较差电堆的功率波动,维持系统一致性,有利于系统长期稳定运行。
基金National Natural Science Foundation of China,Grant No.52375264.
文摘In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
基金supported by the National Natural Science Foundation of China(62076185,U1809209)supported by Zhejiang Provincial Natural Science Foundation of China(LY21F020030)+1 种基金Wenzhou Major Scientific and Technological Innovation Project(ZY2019019)Wenzhou Science and Technology Bureau(2018ZG016)。
文摘The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.
文摘Identifying the parameters of photovoltaic(PV)modules is significant for their design and simulation.Because of the instabilities in the weather action and land surface of the earth,which cause errors in measuring,a novel fuzzy representation-based PV module is formulated and developed.In this paper,a novel locomotion-based hybrid salp swarm algorithm(LHSSA)is presented to identify the parameters of PV modules accurately and reliably.In the LHSSA,better leader salps based on particle swarm optimization(PSO)are incorporated to the traditional salp swarm algorithm(SSA)in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA.By this integration,the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region.The proposed LHSSA is investigated on different PV models,i.e.,single-diode(SD),double-diode(DD),and PV module in crisp and fuzzy aspects.By comparing with different algorithms,the comprehensive results affirm that the LHSSA can achieve a highly competitive performance,especially on quality and reliability.
