In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deployi...In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deploying multiple sink nodes in WSNs is an effective strategy to solve this problem.A multi-sink deployment strategy based on improved particle swarm clustering optimization(IPSCO) algorithm for WSNs is proposed in this paper.The IPSCO algorithm is a combination of the improved particle swarm optimization(PSO) algorithm and K-means clustering algorithm.According to the sink nodes number K,the IPSCO algorithm divides the sensor nodes in the whole network area into K clusters based on the distance between them,making the total within-class scatter to minimum,and outputs the center of each cluster.Then,multiple sink nodes in the center of each cluster can be deployed,to achieve the effects of partition network reasonably and deploy multi-sink nodes optimally.The simulation results show that the deployment strategy can prolong the network lifetime.展开更多
The stable structures and energies of Ni clusters were investigated using particle swarm optimization(PSO)combined with simulated annealing(SA).Sutton-Chen many-body potential was used in describing the interatomic in...The stable structures and energies of Ni clusters were investigated using particle swarm optimization(PSO)combined with simulated annealing(SA).Sutton-Chen many-body potential was used in describing the interatomic interactions.The simulation results indicate that the structures of Ni clusters are icosahedral-like and binding energy per atom tends to approach that of bulk materials when the atoms number increases.The stability of Ni clusters depends not only on size but also on symmetrical characterization.The structure stability of Nin clusters increases with the increase of total atom number n.It is also found that there exists direct correlation between stability and geometrical structures of the clusters,and relatively higher symmetry clusters are more stable.From the results of the second difference in the binding energy,the clusters at n=3 is more stable than others,and the magic numbers effect is also found.展开更多
The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and dampe...The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this paper reveals that the formation of quality cluster is heavily predominant on the suitable selection of threshold value. In the above-mentionedpaper Anuradha et al. have used a heuristic approach for fixing the threshold value. Although the outcome of the approach is acceptable, however, the approach is purely based on random selection and has no basis to claim the acceptability in general. In this paper a novel method is proposed to optimally compute threshold value using a population based randomized approach known as particle swarm optimization (PSO). Simulations are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype data set and the results of the cluster quality are compared with the fixed approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used with confidence.展开更多
Taking 98 earthquake swarms occurred in Xinjiang during 1972-1992 as examples,and & parameters (e. g. U,K, p and the maximum energy rate of earthquake sequence etc.)as the characteristic quantity in earthquakeswar...Taking 98 earthquake swarms occurred in Xinjiang during 1972-1992 as examples,and & parameters (e. g. U,K, p and the maximum energy rate of earthquake sequence etc.)as the characteristic quantity in earthquakeswarrn pattern observation, the author made a numerical cluster by PP cluster analysis method. The results indicate that those 98 earthquake swarms can be divided into 4 types as A, B, C, D. There are 24 swarms in typeA, among which strong shocks occur nearby after 18 swarms in the coming 12 months.Among 61 earthquakeswarms in type C and D, strong shocks occur nearby only after 7 swarms in the same time period. The occurrence rate of strong shocks only takes 3/11 in type B swarms. No doubt, PP cluster analysis method can effectively distinguish precursory swarms (type A) and correctly judge the short-and medium-term trend in the areaaround the earthquake swarms. Being a new and useful classification, PP cluster provides a wide application tothe identification of the type of earthquake sequence.展开更多
TCPSO (Two-swarm Cooperative Particle Swarm Optimization) has been proposed by Sun and Li in 2014. TCPSO divides the swarms into two groups with different migration rules, and it has higher performance for high-dimens...TCPSO (Two-swarm Cooperative Particle Swarm Optimization) has been proposed by Sun and Li in 2014. TCPSO divides the swarms into two groups with different migration rules, and it has higher performance for high-dimensional nonlinear optimization problems than traditional PSO and other modified method of PSO. This paper proposes a particle swarm optimization by modifying TCPSO to avoid inappropriate convergence onto local optima. The quite feature of the proposed method is that two kinds of subpopulations constructed based on the scheme of TCPSO are divided into some clusters based on distance measure, k-means clustering method, to maintain both diversity and centralization of search process are maintained. This paper conducts numerical experiments using several types of functions, and the experimental results indicate that the proposed method has higher performance than the TCPSO for large-scale optimization problems.展开更多
Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one...Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.展开更多
This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improv...This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and in- creases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clus- tering problem, which can be useful for the niches＇ forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters auto- matically and accurately.展开更多
In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Us...In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.展开更多
Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which i...Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.展开更多
This paper presents a new algorithm for clustering a large amount of data.We improved the ant colony clustering algorithm that uses an ant’s swarm intelligence,and tried to overcome the weakness of the classical clus...This paper presents a new algorithm for clustering a large amount of data.We improved the ant colony clustering algorithm that uses an ant’s swarm intelligence,and tried to overcome the weakness of the classical cluster analysis methods.In our proposed algorithm,improvements in the efficiency of an agent operation were achieved,and a new function "cluster condensation" was added.Our proposed algorithm is a processing method by which a cluster size is reduced by uniting similar objects and incorporating them into the cluster condensation.Compared with classical cluster analysis methods,the number of steps required to complete the clustering can be suppressed to 1% or less by performing this procedure,and the dispersion of the result can also be reduced.Moreover,our clustering algorithm has the advantage of being possible even in a small-field cluster condensation.In addition,the number of objects that exist in the field decreases because the cluster condenses;therefore,it becomes possible to add an object to a space that has become empty.In other words,first,the majority of data is put on standby.They are then clustered,gradually adding parts of the standby data to the clustering data.The method can be adopted for a large amount of data.Numerical experiments confirmed that our proposed algorithm can theoretically applied to an unrestricted volume of data.展开更多
Among the bio-inspired techniques,PSO-based clustering algorithms have received special attention. An improved method named Particle Swarm Optimization (PSO) clustering algorithm based on cooperative evolution with mu...Among the bio-inspired techniques,PSO-based clustering algorithms have received special attention. An improved method named Particle Swarm Optimization (PSO) clustering algorithm based on cooperative evolution with multi-populations was presented. It adopts cooperative evolutionary strategy with multi-populations to change the mode of traditional searching optimum solutions. It searches the local optimum and updates the whole best position (gBest) and local best position (pBest) ceaselessly. The gBest will be passed in all sub-populations. When the gBest meets the precision,the evolution will terminate. The whole clustering process is divided into two stages. The first stage uses the cooperative evolutionary PSO algorithm to search the initial clustering centers. The second stage uses the K-means algorithm. The experiment results demonstrate that this method can extract the correct number of clusters with good clustering quality compared with the results obtained from other clustering algorithms.展开更多
基金the Key Project of the National Natural Science Foundation of China(No.61134009)National Natural Science Foundations of China(Nos.61473077,61473078)+4 种基金Program for Changjiang Scholars from the Ministry of Education,ChinaSpecialized Research Fund for Shanghai Leading Talents,ChinaProject of the Shanghai Committee of Science and Technology,China(No.13JC1407500)Innovation Program of Shanghai Municipal Education Commission,China(No.14ZZ067)the Fundamental Research Funds for the Central Universities,China(No.15D110423)
文摘In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deploying multiple sink nodes in WSNs is an effective strategy to solve this problem.A multi-sink deployment strategy based on improved particle swarm clustering optimization(IPSCO) algorithm for WSNs is proposed in this paper.The IPSCO algorithm is a combination of the improved particle swarm optimization(PSO) algorithm and K-means clustering algorithm.According to the sink nodes number K,the IPSCO algorithm divides the sensor nodes in the whole network area into K clusters based on the distance between them,making the total within-class scatter to minimum,and outputs the center of each cluster.Then,multiple sink nodes in the center of each cluster can be deployed,to achieve the effects of partition network reasonably and deploy multi-sink nodes optimally.The simulation results show that the deployment strategy can prolong the network lifetime.
基金Project(60371046)supported by the National Natural Science Foundation of China
文摘The stable structures and energies of Ni clusters were investigated using particle swarm optimization(PSO)combined with simulated annealing(SA).Sutton-Chen many-body potential was used in describing the interatomic interactions.The simulation results indicate that the structures of Ni clusters are icosahedral-like and binding energy per atom tends to approach that of bulk materials when the atoms number increases.The stability of Ni clusters depends not only on size but also on symmetrical characterization.The structure stability of Nin clusters increases with the increase of total atom number n.It is also found that there exists direct correlation between stability and geometrical structures of the clusters,and relatively higher symmetry clusters are more stable.From the results of the second difference in the binding energy,the clusters at n=3 is more stable than others,and the magic numbers effect is also found.
文摘The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this paper reveals that the formation of quality cluster is heavily predominant on the suitable selection of threshold value. In the above-mentionedpaper Anuradha et al. have used a heuristic approach for fixing the threshold value. Although the outcome of the approach is acceptable, however, the approach is purely based on random selection and has no basis to claim the acceptability in general. In this paper a novel method is proposed to optimally compute threshold value using a population based randomized approach known as particle swarm optimization (PSO). Simulations are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype data set and the results of the cluster quality are compared with the fixed approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used with confidence.
文摘Taking 98 earthquake swarms occurred in Xinjiang during 1972-1992 as examples,and & parameters (e. g. U,K, p and the maximum energy rate of earthquake sequence etc.)as the characteristic quantity in earthquakeswarrn pattern observation, the author made a numerical cluster by PP cluster analysis method. The results indicate that those 98 earthquake swarms can be divided into 4 types as A, B, C, D. There are 24 swarms in typeA, among which strong shocks occur nearby after 18 swarms in the coming 12 months.Among 61 earthquakeswarms in type C and D, strong shocks occur nearby only after 7 swarms in the same time period. The occurrence rate of strong shocks only takes 3/11 in type B swarms. No doubt, PP cluster analysis method can effectively distinguish precursory swarms (type A) and correctly judge the short-and medium-term trend in the areaaround the earthquake swarms. Being a new and useful classification, PP cluster provides a wide application tothe identification of the type of earthquake sequence.
文摘TCPSO (Two-swarm Cooperative Particle Swarm Optimization) has been proposed by Sun and Li in 2014. TCPSO divides the swarms into two groups with different migration rules, and it has higher performance for high-dimensional nonlinear optimization problems than traditional PSO and other modified method of PSO. This paper proposes a particle swarm optimization by modifying TCPSO to avoid inappropriate convergence onto local optima. The quite feature of the proposed method is that two kinds of subpopulations constructed based on the scheme of TCPSO are divided into some clusters based on distance measure, k-means clustering method, to maintain both diversity and centralization of search process are maintained. This paper conducts numerical experiments using several types of functions, and the experimental results indicate that the proposed method has higher performance than the TCPSO for large-scale optimization problems.
基金National Natural Science Foundations of China(No. 61103175,No. 11141005)Technology Innovation Platform Project of Fujian Province,China (No. 2009J1007)+1 种基金Key Project Development Foundation of Education Committee of Fujian Province,China (No.JA11011)Project Development Foundations of Fuzhou University,China (No. 2010-XQ-21,No. XRC-1037)
文摘Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.
基金supported by the National Natural Science Foundation of China (708710157103100271171030)
文摘This paper introduces niching particle swarm optimiza- tion （nichePSO） into clustering analysis and puts forward a cluster- ing algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and in- creases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clus- tering problem, which can be useful for the niches＇ forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters auto- matically and accurately.
文摘In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.
基金This work was supported by the National Grand Fundamental Research"973"Programof China (No.2004CB719401)
文摘Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.
基金Project (No.18510132) supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research
文摘This paper presents a new algorithm for clustering a large amount of data.We improved the ant colony clustering algorithm that uses an ant’s swarm intelligence,and tried to overcome the weakness of the classical cluster analysis methods.In our proposed algorithm,improvements in the efficiency of an agent operation were achieved,and a new function "cluster condensation" was added.Our proposed algorithm is a processing method by which a cluster size is reduced by uniting similar objects and incorporating them into the cluster condensation.Compared with classical cluster analysis methods,the number of steps required to complete the clustering can be suppressed to 1% or less by performing this procedure,and the dispersion of the result can also be reduced.Moreover,our clustering algorithm has the advantage of being possible even in a small-field cluster condensation.In addition,the number of objects that exist in the field decreases because the cluster condenses;therefore,it becomes possible to add an object to a space that has become empty.In other words,first,the majority of data is put on standby.They are then clustered,gradually adding parts of the standby data to the clustering data.The method can be adopted for a large amount of data.Numerical experiments confirmed that our proposed algorithm can theoretically applied to an unrestricted volume of data.
基金National Natural Science Foundation of China ( No.60873058)Science and Technology Project of Shandong Province of China (No.2009GG10001008)Soft Science Research Project, China (No.2009RKA285)
文摘Among the bio-inspired techniques,PSO-based clustering algorithms have received special attention. An improved method named Particle Swarm Optimization (PSO) clustering algorithm based on cooperative evolution with multi-populations was presented. It adopts cooperative evolutionary strategy with multi-populations to change the mode of traditional searching optimum solutions. It searches the local optimum and updates the whole best position (gBest) and local best position (pBest) ceaselessly. The gBest will be passed in all sub-populations. When the gBest meets the precision,the evolution will terminate. The whole clustering process is divided into two stages. The first stage uses the cooperative evolutionary PSO algorithm to search the initial clustering centers. The second stage uses the K-means algorithm. The experiment results demonstrate that this method can extract the correct number of clusters with good clustering quality compared with the results obtained from other clustering algorithms.
