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Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm

APPLIED SOFT COMPUTING 2021年第PartB期113卷 107924-107924页

作者： Li, Yue Chu, Xiaoquan Tian, Dong Feng, Jianying Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： K-means clustering algorithm particle swarm optimization algorithm Adaptive parameter learning Mixed data Customer segmentation

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Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm

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ENERGY 2021年 214卷 119022-119022页

作者： Liu, Ming Wang, Shan Yan, Junjie Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

The accommodation of high-penetration renewable power poses a considerable challenge to power grids. Coal-fired combined heat and power (CHP) stations are forced to enhance their operational flexibility by applying heat-power decoupling technologies. Power-to-heat devices, including electric boilers and heat pumps, are capable to enhance the operational flexibility of coal-fired CHP stations. The problem regarding the operation scheduling of a CHP station with multiple CHP units and power-to-heat devices is addressed in this study. Operation optimization models integrated with detail CHP unit models are developed, and the particle swarm optimization algorithm is utilized as the optimization algorithm. Then, a case study are carried out. Results show that the unequal distribution of heating and power loads among coal-fired CHP units can decrease the total irreversibility caused by heating steam pressure regulation. The operation scheduling method provided in this study can decrease the total coal consumption by 14.14 and 14.70 t/day for the CHP station integrated with an electric boiler and a heat pump, respectively. As a result, 1204.7 and 1252.44 ton CO2, and an additional similar to 182 and-190 kUSD/year can be saved for the reference CHP station integrated with an electric boiler and a heat pump, respectively. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： CHP Energy saving Flexibility Operation scheduling particle swarm optimization algorithm

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A Quantum particle swarm optimization algorithm with Available Transfer Capability 19

A Quantum Particle Swarm Optimization Algorithm with Availab...

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19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Qu Liping Meng Yan Li Dongheng Xue Hai-bo Beihua Univ Engn Training Ctr Jilin 132011 Jilin Peoples R China Jilin Prov Ind Technician Coll Dept Elect Engn Jilin 132011 Jilin Peoples R China State Grid Jilin Elect Power Co Ltd Shulan Power Supply Co Jilin 132011 Jilin Peoples R China Jilin Municipal Govt Jilin 132011 Jilin Peoples R China

ISBN: (数字)9781728197241

ISBN: (纸本)9781728197241

Quantum particle swarm algorithm integrated the quantum behavior with particle swarm optimization algorithm, is used to settle the majorization question of calculating available transmission capability. And by using the software of Matlab to IEEE-30 bus system as an example of the simulation, after comparing the simulation results with the traditional particle swarm optimization algorithm results, we dissected the optimization performance and convergence speed of the above two algorithms, and verify the effectiveness of quantum particle swarm algorithm to settle the majorization question of the available transmission capability.

关键词： available transmission capacity particle swarm optimization algorithm quantum particle swarm optimization algorithm

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Novel particle swarm optimization algorithm Based on President Election: Applied to a Renewable Hybrid Power System Controller

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 97-109页

作者： Yahyazadeh, M. Johari, M. S. HosseinNia, S. H. Vali E Asr Univ Rafsanjan Fac Engn Rafsanjan Iran Fac 3mE Dept Precis & Microsyst Engn Mekelweg 2 NL-2628 CD Delft Netherlands

particle swarm optimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election particle swarm optimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.

关键词： Hybrid optimization algorithm Chaotic Function Hybrid Power System particle swarm optimization algorithm President Election algorithm

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A Novel Competitive particle swarm optimization algorithm Based on Levy Flight

A Novel Competitive Particle Swarm Optimization Algorithm Ba...

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2019 Chinese Intelligent Systems Conference

作者： Ning, Yao Liu, Zhongxin Chen, Zengqiang Zhao, Chen Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Key Lab Intelligent Robot Tianjin Tianjin 300350 Peoples R China

ISBN: (纸本)9789813296848;9789813296824;9789813296817

The particle swarm optimization is a classical optimization algorithm (PSO) that has been applied to various fields. It implements simple but efficient evolution operators to search for optimums in parameter space. However, it could not reach a good balance between global search and local search when it comes to multimodal' problem. Levy flight is a kind of stochastic process with scale invariant, turned out to accord to animals' behavior. This paper utilizes the levy flight operator to enhance the PSO's global search ability during the iteration, which is based on the hierarchy structure of swarms. Therefore, a novel competitive particle swarm optimization based on levy flight (CLFPSO) is proposed. A number of benchmarks has been tested on the CLPSO with other five typical PSO algorithms. The experimental results show that the proposed algorithm could reach more outstanding and accurate consequences compared with other algorithms.

关键词： particle swarm optimization algorithm Hierarchy structure Levy flight A number of benchmarks

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Indoor positioning system based on particle swarm optimization algorithm

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MEASUREMENT 2019年 134卷 908-913页

作者： Guo, Hang Li, Huixia Xiong, Jian Yu, Min Nanchang Univ Sch Resources Environm & Chem Engn Key Lab Poyang Lake Environm & Resource Utilizat Minist Educ Nanchang 330031 Jiangxi Peoples R China Nanchang Univ Nanchang 330031 Jiangxi Peoples R China Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada Jiangxi Normal Univ Nanchang 330022 Jiangxi Peoples R China

In recent years, wireless sensor networks localization becomes a crucial method in the indoor positioning. Following the frontiers of technology, we studied on ZigBee wireless sensor network. Since the parameters of the path loss model are difficult to be estimated by the ordinary methods, the particle swarm optimization (PSO) method is proposed in this paper to simulate the parameter estimation in the indoor environment. The Texas Instruments CC2530 chip was also taken to build a ZigBee wireless sensor network. The data collected from ZigBee wireless sensor network experiment could be used to estimate the model parameters. PSO algorithm for fitting the signal attenuation curve removed the poor experimental data, and the output model fit well with the signal attenuation curve. Experimental results demonstrate that the PSO algorithm works well, clear, easy to understand, and has a high reliability. Using the parametric model to locate the user's position, and with the weighted K-Nearest Neighbor algorithm, the two-dimensional (2D) positioning was improved effectively. The standard deviation of 2D positioning is 1.15 m, so the model has practical value. Through the experiment and analyzing the data, it is verified that the proposed PSO algorithm in this paper is better than the previous methods we presented. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： ZigBee Path loss model particle swarm optimization algorithm Parameter estimation K-Nearest Neighbor algorithm

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A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems

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NEUROCOMPUTING 2019年 330卷 380-393页

作者： Li, Dapu Li, Kenli Liang, Jie Ouyang, Aijia Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Zunyi Normal Univ Sch Informat Engn Zunyi 563006 Guizhou Peoples R China

An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Genetic algorithm Load balancing Metal solidification Molecular dynamics simulation particle swarm optimization algorithm Supercomputer

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Research on speed optimization model of pickling and tandem cold rolling mill based on particle swarm optimization algorithm

Research on speed optimization model of pickling and tandem ...

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第33届中国控制与决策会议

作者： ZHU Xiao-yan LIU Yong ZHANG Shi-bang LI Song-hua CAO Jiao-zhao HU Yun-jian Engineering Training Center Shenyang Jianzhu University School of Mechanical Engineering Shenyang Jianzhu University Information & Control Engineering Faculty Shenyang Jianzhu University

In the combined continuous pickling and tandem cold rolling line,the unstable speeds of four sections will not only decrease production efficiency,but also cause equipment *** order to solve the problem of great speed fluctuations in production line,the pickling section and rolling section are studied as a whole in this *** specific works include the analysis of the speed characteristics of each section and design of the objective ***,the optimized speed of each section is calculated by a particle swarm optimization *** to the speed comparison before and after optimization,it is found that the optimized speed does not fluctuate sharply,the acceleration and deceleration of the production line are stable,and the abundance values of loopers are controlled within a reasonable *** application shows that the optimized speeds can fluctuate well within a certain range,which reduces equipment wear and improves production *** proposed speed optimization model is suitable for industrial promotion.

关键词： Speed optimization particle swarm optimization algorithm Pickling and tandem cold rolling

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An Improved DV-Hop Scheme Based on Path Matching and particle swarm optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2019年第4期104卷 1301-1320页

作者： Shi, Qinqin Xu, Qiang Zhang, Jianping Shanghai Inst Technol Sch Comp Sci & Informat Engn Shanghai 201418 Peoples R China Suzhou Zhongke Adv Technol Res Inst Co Ltd Suzhou 215123 Peoples R China Sino Parking Tech Co Ltd Shanghai 200127 Peoples R China

Distance vector hop (DV-Hop) is a frequently-used localization technology for wireless sensor networks. The traditional DV-Hop scheme estimates the node-anchor distance depending on the hop-count between the network nodes. It is the advantage of the scheme because no costive direct range finding is needed, but it still is the disadvantage of the scheme because the heterogeneity of network topology will make the node-anchor distance estimation precision poor and the localization precision unstable. Since the heterogeneity of network topology is very common due to random node deployment in real applications, the effectiveness of DV-Hop scheme in these applications becomes difficult to confirm and the algorithm needs applicability improvement. Focusing on above problem of the traditional DV-Hop, improved strategies are provided.A path matching algorithm is presented to find out the optimal anchor-anchor shortest path, which is used to determine the average hop distance between anunknown node and its target anchor independently, aiming at making the estimated node-anchor distance as close as possible to the real distance;furtherly, a modified particle swarm optimization algorithm is presented to optimize the initial position of each unknown node, aiming at improving the whole node localization accuracy of the network. Simulations are carried out on different network topologies both in square area and in C-shaped area, and comparisonsaremade for our scheme with the traditional DV-Hop and the other three existed representative improved schemes. Results show that our scheme has better performance both on distance estimation accuracy and on average nodelocalization accuracy.

关键词： DV-Hop Node localization Path matching Trilateration particle swarm optimization algorithm

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Sensorless rotor field direct orientation controlled induction motor drive with particle swarm optimization algorithm flux observer

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2019年第2期38卷 692-705页

作者： Luo, Yung-Chang Huang, Wei-An Natl Chin Yi Univ Technol Dept Elect Engn 57Sec 2Zhongshan Rd Taichung 41170 Taiwan

A speed estimation scheme based on the particle swarm optimization algorithm flux observer is proposed for a sensorless rotor field direct orientation controlled induction motor drive. The stator current and rotor flux was used to establish both the rotor field direct orientation controlled induction motor drive and the rotor-flux observer. The estimated synchronous angle position was acquired from a current-and-voltage parallel-model rotor estimator for implementation of the exact coordinate transformation to achieve a perfect rotor field direct orientation controlled induction motor drive. The rotor-flux observer was designed using the Lyapunov stability theory, and the estimated rotor speed was derived from the developed the rotor-flux estimator;this estimated speed was unaffected by the slip speed. The gain matrix of this flux observer was obtained using the particle swarm optimization algorithm because it is simple, achieves rapid convergence, and is suitable for a variety of conditions. This system was simulated using the MATLAB/Simulink (R) toolbox, and all the control algorithms were realized by a TI DSP 6713-and-F2812 control card. Both simulation and experimental results confirmed the effectiveness of the proposed approach.

关键词： Sensorless rotor field direct orientation control flux observer flux estimator particle swarm optimization algorithm

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