Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image f...Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.展开更多
In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal...In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.展开更多
Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its forma...Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.展开更多
As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimizat...As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.展开更多
In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)...In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.展开更多
At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and ...At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.展开更多
In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporat...In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.展开更多
目的针对生鲜产品冷链配送环节存在的配送成本高、产品易腐坏等问题,研究考虑新鲜度的生鲜冷链物流同时取送货车辆路径优化。方法首先考虑同时取送货的场景,以及生鲜产品在不同场景下的新鲜度衰减速度,以车辆使用成本、货损成本、时间...目的针对生鲜产品冷链配送环节存在的配送成本高、产品易腐坏等问题,研究考虑新鲜度的生鲜冷链物流同时取送货车辆路径优化。方法首先考虑同时取送货的场景,以及生鲜产品在不同场景下的新鲜度衰减速度,以车辆使用成本、货损成本、时间惩罚成本之和最小为目标,建立考虑新鲜度的生鲜冷链物流同时取送货车辆路径优化模型;其次,基于此模型,设计一种带大邻域搜索思想的头脑风暴算法(Brain storm optimization with large neighborhood search algorithm,BSO-LNS)进行求解;然后,通过与CPLEX求解器、遗传算法的对比分析,验证所建模型的合理性及求解算法的有效性;最后,求解实际冷链物流企业的算例,验证本文模型在实际冷链物流配送中的应用价值。结果基于不同规模的算例,与CPLEX求解器、遗传算法相比,所设计的算法的求解效果更好,同时其求解速度更快。结论所提模型、算法可有效减少生鲜产品品质损耗,同时兼顾对总成本的控制,进而为生鲜物流企业提供了方法参考和决策依据。展开更多
为了更好适应下一代通信网络以内容为中心的特性,在云接入网络(Cloud Radio Access Network,Cloud-RAN)中考虑射频拉远头(Remote Radio Heads,RRHs)具备缓存功能也变得必要。本文考虑在Cloud-RAN中设计优化算法,并通过有效设计缓存方案...为了更好适应下一代通信网络以内容为中心的特性,在云接入网络(Cloud Radio Access Network,Cloud-RAN)中考虑射频拉远头(Remote Radio Heads,RRHs)具备缓存功能也变得必要。本文考虑在Cloud-RAN中设计优化算法,并通过有效设计缓存方案减少系统传输时延。基于混合式自动重传请求(hybrid automatic repeat request, HARQ)的重传机制,前程链路与下行链路频谱信道的正交性,系统采用马尔可夫链理论建立了最小化系统传输时延的优化问题。考虑只能通过递归方式得到优化目标函数表达式,头脑风暴优化(brain storm optimization, BSO)算法被引入解决非凸问题,仿真结果表明,该优化算法可以更有效地减少系统传输时延,满足未来通信需求。展开更多
作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference br...作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference brain storm optimization based on clustering in objective space,DBSO–OS)算法.算法通过对目标空间的聚类替代对决策空间的聚类,减小了算法的运算复杂度;采用差分变异代替高斯变异来增加种群的多样性.多个测试函数的仿真结果表明,目标空间聚类的差分头脑风暴算法不仅提高了算法的寻优速度,而且提高了算法的寻优精度.文中进一步分析了参数对算法性能的影响,设计了最佳参数选择方案,并用于对实际热电联供经济调度问题的求解,验证了算法的实用性.展开更多
文摘Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.
文摘In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.
基金This work was partially supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61375121)the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301)+1 种基金the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Jiangsu Province(BE2020633)the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.
基金supported by the National Natural Science Foundation of China(61876089,61403206,61876185,61902281)the opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS302)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20141005)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(14KJB520025)the Engineering Research Center of Digital Forensics,Ministry of Education,and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.
基金supported by Natural Science Foundation of Jiangsu Province(Grant No.BK20141005)by Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.14KJB520025).
文摘In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.
基金supported in part by the National Natural Science Foundation of China(Nos.62173356 and 61703320)the Science and Technology Development Fund(FDCT),Macao SAR(No.0019/2021/A)+3 种基金Shandong Province Outstanding Youth Innovation Team Project of Colleges and Universities(No.2020RWG011)Natural Science Foundation of Shandong Province(No.ZR202111110025)China Postdoctoral Science Foundation Funded Project(No.2019T120569)the Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC).
文摘At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.
基金supported by the National Natural Science Foundation of China (61761004)the Natural Science Foundation of Guangxi Province, China (2019GXNSFAA245045)。
文摘In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.
文摘目的针对生鲜产品冷链配送环节存在的配送成本高、产品易腐坏等问题,研究考虑新鲜度的生鲜冷链物流同时取送货车辆路径优化。方法首先考虑同时取送货的场景,以及生鲜产品在不同场景下的新鲜度衰减速度,以车辆使用成本、货损成本、时间惩罚成本之和最小为目标,建立考虑新鲜度的生鲜冷链物流同时取送货车辆路径优化模型;其次,基于此模型,设计一种带大邻域搜索思想的头脑风暴算法(Brain storm optimization with large neighborhood search algorithm,BSO-LNS)进行求解;然后,通过与CPLEX求解器、遗传算法的对比分析,验证所建模型的合理性及求解算法的有效性;最后,求解实际冷链物流企业的算例,验证本文模型在实际冷链物流配送中的应用价值。结果基于不同规模的算例,与CPLEX求解器、遗传算法相比,所设计的算法的求解效果更好,同时其求解速度更快。结论所提模型、算法可有效减少生鲜产品品质损耗,同时兼顾对总成本的控制,进而为生鲜物流企业提供了方法参考和决策依据。
文摘作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference brain storm optimization based on clustering in objective space,DBSO–OS)算法.算法通过对目标空间的聚类替代对决策空间的聚类,减小了算法的运算复杂度;采用差分变异代替高斯变异来增加种群的多样性.多个测试函数的仿真结果表明,目标空间聚类的差分头脑风暴算法不仅提高了算法的寻优速度,而且提高了算法的寻优精度.文中进一步分析了参数对算法性能的影响,设计了最佳参数选择方案,并用于对实际热电联供经济调度问题的求解,验证了算法的实用性.
