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Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by wolf pack algorithm

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ENERGIES 2018年第6期11卷 1449页

作者： Zhang, Xing North China Elect Power Univ Dept Econ Management Baoding 071003 Peoples R China

Accurate short-term load forecasting is of momentous significance to ensure safe and economic operation of quick-change electric bus (e-bus) charging stations. In order to improve the accuracy and stability of load prediction, this paper proposes a hybrid model that combines fuzzy clustering (FC), least squares support vector machine (LSSVM), and wolf pack algorithm (WPA). On the basis of load characteristics analysis for e-bus charging stations, FC is adopted to extract samples on similar days, which can not only avoid the blindness of selecting similar days by experience, but can also overcome the adverse effects of unconventional load data caused by a sudden change of factors on training. Then, WPA with good global convergence and computational robustness is employed to optimize the parameters of LSSVM. Thus, a novel hybrid load forecasting model for quick-change e-bus charging stations is built, namely FC-WPA-LSSVM. To verify the developed model, two case studies are used for model construction and testing. The simulation test results prove that the proposed model can obtain high prediction accuracy and ideal stability.

关键词： short-term load forecasting electric bus charging station fuzzy clustering least squares support vector machine wolf pack algorithm

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Approach to Port Logistics Optimization in Promoting One Belt One Road Policy Based on Improved wolf pack algorithm

Approach to Port Logistics Optimization in Promoting One Bel...

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第十届（2018）金融风险与公司金融国际研讨会

作者： Li Guangqiang Zhang Qing Yu Zhihao Zhang Zhaobao Liu Qi Xu Xueliu College of Marine Electrical Engineering Dalian Maritime University Faculty of Management and Economics Dalian University of Technology

There are many optimization problems in port logistics automation in promoting One Belt One Road *** intelligent algorithms are regarded as promising approach to complex port logistics optimization *** pack algorithm(WPA) is a newly proposed algorithm,which has been widely applied in many fields such as automated container terminals,port logistics optimization,and regional economic ***,defects still exist in *** at the defects of WPA,such as fixed scouting direction and R artificial wolves with worst function values will directly be removed,an improved wolf pack algorithm named RHWPA is proposed based on random scouting direction and hunger value *** each scouting,fixed directions h is selected randomly within a certain *** can avoid the scout wolves falling into local optima,and improve the convergence speed and optimization *** further improve the convergence speed,the hunger value strategy is applied instead of the stronger-survive renewing rule for the wolf *** results on the benchmark functions show that RHWPA is feasible and *** work is expected to provide inspiration and help to solving corresponding port logistics optimization problems perfectly on promotion One Belt One Road policy.

关键词： wolf pack algorithm optimization function optimization One Belt One Road

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A Novel Hybrid Based on wolf pack algorithm and Differential Evolution algorithm 10

A Novel Hybrid Based on Wolf Pack Algorithm and Differential...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Chen, Xiayang Tang, Chaojing Wang, Jian Zhang, Lei Liu, Yi Natl Univ Def Technol Coll Elect Sci Changsha Hunan Peoples R China

ISBN: (纸本)9781538636756

The wolf pack algorithm (WPA) is a new optimal algorithm with good performance in optimation, but there is still room for improvement of its convergence. Aiming at enhancing the ability of the local search and increasing the individual diversity in the population of the wolf pack algorithm, a novel hybrid based on wolf pack algorithm and Differential Evolution (DE) was proposed, which named WPA/DE for short. Six standard benchmark functions are applied to verify the effects of these improvements. It is shown that the performance of WPA/DE is superior to, or at least highly competitive with, the standard WPA and other swarm intelligence optimal algorithm, such as GA, DE, PSO, and ABC in most situations.

关键词： wolf pack algorithm Differential Evolution swarm intelligence evolutionary computation

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An Adaptive, Discrete Space Oriented wolf pack Optimization algorithm for a Movable Wireless Sensor Network

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SENSORS 2019年第19期19卷 4320-4320页

作者： Wang, Dongxing Wang, Huibo Ban, Xiaojuan Qian, Xu Ni, Jingxiu China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Beijing Adv Innovat Ctr Mat Genome Engn Beijing 100083 Peoples R China Beijing Union Univ Engn Integrated Expt Teaching Demonstrat Ctr Beijing 100101 Peoples R China

Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this paper proposes an adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network (DSO-WPOA). Firstly, a strategy of adaptive expansion based on a minimum overlapping full-coverage model is designed to achieve minimum overlap and no-gap coverage for the monitoring area. Moreover, the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) is improved to optimize the movable wireless sensor network, which is a discrete space oriented problem. This improvement includes the usage of a target-node probability matrix and the design of an adaptive step size method, both of which work together to enhance the convergence speed and global optimization ability of the algorithm. Theoretical research and experimental results indicate that compared with the coverage algorithm based on particle swarm optimization (PSO-WSN) and classical virtual force algorithm, the newly proposed algorithm possesses the best coverage rate, better stability, acceptable performance in terms of time, advantages in energy savings, and no gaps.

关键词： wireless sensor network coverage rate wolf pack algorithm optimization probability matrix adaptive step size

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Coverage optimization of camera network for continuous deformable object

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MECHATRONICS 2023年第1期95卷

作者： Li, Chang Chen, Xi Chai, Li Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Hubei Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

In this paper, a deformable object is considered for camera network deployment with the aim of visual coverage. The object contour is discretized into sampled points as meshes, and the deformation is represented by continuous trajectories for the sampled points. To reduce the computational complexity, some feature points are carefully selected to represent the continuous deformation process. The visual coverage for the deformable object is transferred to cover the specific feature points. In particular, the vertexes of a rectangle that encloses the entire deformation trajectory of every sampled point on the object contour are chosen as the feature points. An improved wolf pack algorithm is then proposed to solve the optimization problem. Finally, both simulation and experimental results are given to demonstrate the effectiveness of the proposed deployment method of camera network.

关键词： Camera network deployment Visual coverage Deformable object wolf pack algorithm

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Artificial intelligence-based optimization for ring-opening metathesis polymerization of proton exchange membrane

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JOURNAL OF POLYMER RESEARCH 2023年第11期30卷 418-418页

作者： Feng, Zhiming Jin, Shan Xiang, Hang Li, Da Sun, Shuai Zhang, Huagui Chen, Yi Imperial Coll London Chem Engn London England Univ Portsmouth Fac Business & Law Portsmouth England Paul Scherrer Inst Lab Bioenergy & Catalysis CH-5232 Villigen Switzerland Harbin Inst Technol Sch Environm State Key Lab Urban Water Resource & Environm Harbin Peoples R China Nanjing Univ Sci & Technol Natl Key Lab Transient Phys Nanjing 210094 Peoples R China Fujian Normal Univ Coll Chem & Mat Sci Fujian Key Lab Polymer Sci Fuzhou 350007 Peoples R China Newcastle Univ Sch Engn Newcastle Upon Tyne England

The proton exchange membrane (PEM) is one of the significant components in PEM fuel cells. However, conventional synthesis experiments for proton exchange membrane (PEM) require considerable workload and time due to complicated conditions and various influencing factors. Here we initially utilized artificial intelligence (AI) techniques based on the artificial wolf pack algorithm (AWPA) to optimize the synthesis reaction conditions of the ring-opening metathesis polymerization (ROMP) reaction of norbornene derivatives for PEM preparation. An empirical model was established based on four variables, including temperature, reaction time, catalyst amount and ratio of reactants, with two fitness functions, including molecular weight (MW) and molecular weight distribution (MWD). Four trend indices were used, including the mean average precision (mAP), the mean, standard deviation (mSTD), the moving mean of the average precision (mmAP) and the moving mean of standard derivation (mmSD). The theoretical optimum values of operating conditions were obtained successfully, including reactant ratio (0.71), temperature (41.23 degrees C), catalyst content (0.20) and reaction time (47.94 min). The method in this paper helps optimize PEM preparation conditions and guides a database for AI-aid ROMP reactions.

关键词： Proton exchange membrane (PEM) Fuel cell Ring-opening metathesis polymerization (ROMP) Artificial intelligence (AI) wolf pack algorithm

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Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Generative Adversarial Network

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1104-1114页

作者： Qianyu Zhao Wenlong Liao Shouxiang Wang Jayakrishnan Radhakrishna Pillai the Key Laboratory of Smart Grid of Ministry of Education and Tianjin Key Laboratory of Power System Simulation and Control Tianjin UniversityTianjinChina the Department of Energy Technology Aalborg UniversityAalborgDenmark

The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution *** this paper,a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network(IGAN).Firstly,both real and predicted data are used to train the IGAN consisting of a discriminator and a *** noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario ***,a new improved wolf pack algorithm(IWPA)is presented to solve the formulated robust voltage control model,since the accuracy of the solutions obtained by traditional methods is *** simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads,which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional ***,IWPA has a better performance than traditional methods in terms of convergence speed,accuracy,and stability for robust voltage control.

关键词： Robust voltage control uncertainty generative adversarial network wolf pack algorithm

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A wolf pack Optimization Theory Based Improved Density Peaks Clustering Approach

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2020年第6期27卷 1910-1916页

作者： Tian, Jinlong Zhao, Jianping Zhou, Wei Han, Xuming Wang, Limin Changchun Univ Sci & Technol Sch Comp Sci & Engn Changchun 130022 Peoples R China Jinan Univ Coll Informat Sci Technol Guangzhou 510632 Peoples R China Guangdong Univ Finance Sch Internet Finance & Informat Engn Guangzhou 510521 Peoples R China

In view of the problem that the Density Peaks Clustering (DPC) algorithm needs to manually set the parameter cut-off distance (d(c)) we propose a wolf pack optimization theory based Density Peaks Clustering approach (WPA-DPC). Firstly, we introduce d, parameter into the wolf pack algorithm (WPA) to speed up the search. Secondly, we introduce the WPA into the DPC algorithm;the cut-off distance is used as the location of the wolf group. Finally, we make silhouette index in the search process as the fitness value, and the optimal location of the wolf group is the parameter value at the end. The simulation results show that compared with the traditional Density Peaks Clustering algorithm, the proposed algorithm is closer to the true clustering number. According to the evaluation results of silhouette and f-measure, the quality of clustering and the accuracy are greatly improved.

关键词： cut-off distance density peaks clustering evaluation index wolf pack algorithm

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A novel topology optimization of coverage-oriented strategy for wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2021年第4期17卷 p1-14页

作者： Wang, Shuxin You, Hairong Yue, Yinggao Cao, Li Wenzhou Univ Oujiang Coll Wenzhou 325035 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Peoples R China

Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group's wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

关键词： Internet of things wireless sensor networks topology wolf pack algorithm coverage optimization

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A hybrid algorithm on the vessel routing optimization for marine debris collection

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115198-115198页

作者： Duan, Gang Fan, Tao Chen, Xiaohui Chen, Li Ma, Junfeng Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China Lanzhou City Univ Dept Math Lanzhou 730070 Gansu Peoples R China Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA

More and more marine debris not only damages the environment, but also endangers human health and marine life. Since the location of marine debris is not fixed, we first apply the GNOME software to predict the trajectory of marine debris and determine its location. Then, the logistics network is used to optimize the collection of marine debris. A mixed integer linear programming model for debris vessel routing is proposed. The objective function is to minimize the total cost, which includes the fuel cost consisting of is a piecewise linear function, labor cost, and fixed cost consisting of rent and insurance relative to the vessels used, taking into account the vessel weight capacity, fuel tank capacity, and time windows constraint at each debris location. We propose a hybrid algorithm of adaptive large-scale neighborhood search, combined with a wolf pack algorithm. This paper takes Yangtze River Estuary to the East China Sea as an example to verify the proposed model and algorithm. The results show that, compared with the worst-case, the most appropriate collection time can save up to 6.38% of the cost, which is about $75,590 per calendar year. The sensitivity analysis of debris weight and vessel capacity is also performed.

关键词： Marine debris collection Vessel routing optimization GNOME Adaptive large-scale neighborhood search wolf pack algorithm

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