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Optimizing the echo state network with a binary particle swarm optimization algorithm

KNOWLEDGE-BASED SYSTEMS 2015年第Sep.期86卷 182-193页

作者： Wang, Heshan Yan, Xuefeng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

The echo state network (ESN) is a novel and powerful method for the temporal processing of recurrent neural networks. It has tremendous potential for solving a variety of problems, especially real-valued, time-series modeling tasks. However, its complicated topologies and random reservoirs are difficult to implement in practice. For instance, the reservoir must be large enough to capture all data features given that the reservoir is generated randomly. To reduce network complexity and to improve generalization ability, we present a novel optimized ESN (O-ESN) based on binary particle swarm optimization (BPSO). Because the optimization of output weights connection structures is a feature selection problem and PSO has been used as a promising method for feature selection problems, BPSO is employed to determine the optimal connection structures for output weights in the O-ESN. First, we establish and train an ESN with sufficient internal units using training data. The connection structure of output weights, i.e., connection or disconnection, is then optimized through BPSO with validation data. Finally, the performance of the O-ESN is evaluated through test data. This performance is demonstrated in three different types of problems, namely, a system identification and two time-series benchmark tasks. Results show that the O-ESN outperforms the classical feature selection method, least angle regression (LAR) method in that its architecture is simpler than that of LAR. (C) 2015 Elsevier BM. All rights reserved.

关键词： Echo state network Reservoir computing Artificial neural network binary particle swarm optimization algorithm Time-series prediction

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An effective feature selection based cross-project defect prediction model for software quality improvement

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第SUPPL 1期14卷 S154-S172页

作者： Khatri, Yogita Singh, Sandeep Kumar Jaypee Inst Informat Technol Dept Comp Sci Engn & Informat Technol Noida 201309 India

Cross-project defect prediction (CPDP) involves the use of other projects (aka source projects) for training and persuasive model building for a particular project (aka target project). However, the distribution dissimilarity between the two different project data often limits the CPDP model's capability. Several CPDP approaches have been proposed in the literature to combat this distribution gap through instance selection by transferring the knowledge learned from the source to the target project. However, very few have explored transferring knowledge through feature selection (FS). A novel CPDP approach has been proposed consisting of two distinct FS strategies (one non-iterative and one iterative) having a trade-off between the cost and the performance respectively. The first strategy MIC_SM_FS is a non-iterative strategy that selects features that are important and have similar distribution with the corresponding target feature. The feature importance is measured using maximal information coefficient and the feature distribution similarity is calculated using 10 statistical measures. On the other hand, the second strategy BPSO_FS is an iterative strategy that works on optimizing the performance, utilizing the powerful binary particle swarm optimization algorithm for selecting the representative features for CPDP. Both of the proposed strategies have been tested on 26 cross-project experiments based on 8 software projects. From the two proposed strategies, a CPDP model built utilizing BPSO_FS showed better results. Further, to assess its performance, comparison is done with two baseline approaches viz. ALL and ManualDown, within-project defect prediction, and a state-of-the-art CPDP technique TCA+. Statistical results showed the potential of the proposed CPDP approach over the compared approaches.

关键词： Cross-project defect prediction Maximal information coefficient binary particle swarm optimization algorithm Feature selection

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Gene Selection and Classification Method Based on SNR and Multi-loops BPSO 20th

Gene Selection and Classification Method Based on SNR and Mu...

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20th International Conference on Intelligent Computing (ICIC)

作者： Chang, Baofang Sun, Fuxiang Li, Maodong Yuan, Peiyan Zang, Hecang Jin, Hu Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Big Data Engn Lab Teaching Resources & Assessment Xinxiang 453007 Henan Peoples R China Key Lab Artificial Intelligence & Personalized Le Xinxiang 453007 Henan Peoples R China Henan Acad Agr Sci Inst Agr Informat Technol Zhengzhou 450002 Peoples R China Hanyang Univ Dept Elect & Commun Engn Ansan 15588 South Korea

ISBN: (纸本)9789819756889;9789819756896

For gene expression data with a massive amount of redundant data and noise, gene selection methods based on binary particle swarm optimization algorithm (BPSO) is an important method to improve classification performance. However, most BPSO-based methods can only handle finite sets, resulting in more selected genes and lower classification accuracy. This paper proposes a hybrid method SNR-MBPSO that utilizes signal-to-noise ratio (SNR) combined with multi-loops BPSO and various classifiers. It is important to point out that the multi-loops BPSO structure in this paper is first proposed and used to solve gene selection problems. And the multi-loops BPSO shows a remarkable improvement in the selection when it is combined with SNR. What is more, the traditional BPSO is modified by using adaptive weights and an improved bit-value changing strategy. To verify the performance of the proposed method, the SNR-MBPSO is compared with the other seven recently published algorithms in the literature. Experimental results based on nine publicly available gene expression datasets have shown that the proposed method significantly outperforms the state-of-the-art methods in terms of classification accuracy and the number of key genes.

关键词： binary particle swarm optimization algorithm signal-to-noise ratio support vector machine

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Area-to-point heat conduction enhancement using binary particle swarm optimization

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APPLIED THERMAL ENGINEERING 2019年 155卷 449-460页

作者： Cai, Hongwei Li, Xue Xie, Chungang Guo, Kai Liu, Hui Liu, Chunjiang Tianjin Univ State Key Lab Chem Engn Sch Chem Engn & Technol Tianjin 300072 Peoples R China SOA Inst Seawater Desalinat & Multipurpose Utilizat Tianjin 300192 Peoples R China

The area-to-point heat conduction problem is solved using a discrete binary particle swarm optimization (BPSO) algorithm. The distribution of conductive materials is optimized to form a tree-shaped conducting path, which can significantly reduce the maximum temperature of the heat-generating domain. The effects of the conductivity ratio and the quantity of conductive materials on the geometric structure of the conducting path are investigated. As conductivity ratio and filling ratio increase, the maximum temperature decreases. In addition to the conductivity ratio and filling ratio, the heat sink location is found to have an impact on cooling capacity. That is, the maximum temperature decreases with decrease in distance between the hot spot and heat sink. In addition, the structural features of the conducting path produced according to different objectives are characterized.

关键词： Heat conduction enhancement Area-to-point problem Conducting path construction binary particle swarm optimization algorithm

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Multi-Objective optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO algorithm

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ENERGIES 2024年第19期17卷 4877页

作者： Lu, Dan Li, Wenfeng Zhang, Linjuan Fu, Qiang Jiao, Qingtao Wang, Kai State Grid Henan Econ Res Inst Zhengzhou 450000 Peoples R China NARI TECH Nanjing Control Syst Co Ltd Nanjing 210000 Peoples R China

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration.

关键词： binary particle swarm optimization algorithm multi-objective reconstruction distributed power supply genetic algorithm

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A Fault Location Method for Active Distribution Network with DGs 6

A Fault Location Method for Active Distribution Network with...

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6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Bao, Wenhong Fang, Qian Wang, Pin Yan, Wenqi Pan, Peng Yunnan Power Grid Co Ltd Kunming Power Supply Bur Kunming 650011 Yunnan Peoples R China

ISBN: (纸本)9781665432634

The increasing penetration of distributed generation transforms the traditional passive distribution network into an active network, which will result in the flow of fault current changing from unidirectional to bidirectional, and the traditional single-source radial distribution network fault location methods are no longer applicable. In this paper, a fault location method for active distribution network with distributed generation is proposed. First, the correlation matrix corresponding to each power source acting individually is established according to the line topology, and a switching function that reflects the relationship between the fault state of a line segment and the FTU alarm signals is constructed using this matrix as a bridge. Then, the objective function reflecting the similarity between the desired signals and the actual alarm signals is established by combining the minimum set theory, and the fault probability of each segment in the line is calculated by using the BPSO algorithm. Finally, an active distribution network containing 13 nodes is used as an example for simulation and verification. The simulation results not only validate the effectiveness of the proposed method in this paper in the case of single-point fault and double faults, but also prove to be highly fault-tolerant.

关键词： Active Distribution Network Distributed Generation Fault Location Correlation Matrix binary particle swarm optimization algorithm

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A two-stage joint operation and planning model for sizing and siting of electrical energy storage devices considering demand response programs

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 138卷 107912-107912页

作者： Javadi, Mohammad Sadegh Gough, Matthew Mansouri, Seyed Amir Ahmarinejad, Amir Nematbakhsh, Emad Santos, Sergio F. Catalao, Joao P. S. Islamic Azad Univ Dept Elect Engn Shiraz Branch Shiraz Iran Inst Syst & Comp Engn Technol & Sci INESC TEC Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal Islamic Azad Univ Dept Elect Engn Yadegar e Imam Khomeini RAH Shahre Rey Branch Tehran Iran Islamic Azad Univ Dept Elect Engn Cent Tehran Branch Tehran Iran Univ Isfahan Fac Elect Engn Esfahan Iran Portucalense Univ Infante D Henrique P-4200072 Porto Portugal

This study describes a computationally efficient model for the optimal sizing and siting of Electrical Energy Storage Devices (EESDs) in Smart Grids (SG), accounting for the presence of time-varying electricity tariffs due to Demand Response Program (DRP) participation. The joint planning and operation problem for optimal siting and sizing of the EESD is proposed in a two-stage optimization problem. In this regard, the long-term decision variables deal were the size and location of the EESDs and have been considered at the master level while the operating point of the generation units and EESDs is determined by the slave stage of the model utilizing a standard mixed-integer linear programming model. To examine the effectiveness of the model in the slave sub- problem, the operation model is solved for different working days of different seasons. binary particle swarm optimization (BPSO) and binary Genetic algorithm (BGA) have been used at the master level to propose different scenarios for investment in the planning stage. The slave problem optimizes the model in terms of the short-term horizon (day-ahead). Additionally, the slave problem determines the optimal schedule for an SG considering the presence of EESD (with sizes and locations provided by the upper level). The electricity price fluctuates throughout the day, according to a Time-of-Use (ToU) DRP pricing scheme. Moreover, the impacts of DRPs have been addressed in the slave stage. The proposed model is examined on a modified IEEE 24-Bus test system

关键词： Energy Storage Systems Smart Grids Planning Demand Response Programs Time-of-Use Tariffs binary particle swarm optimization algorithm binary Genetic algorithm

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An efficient Bayesian network structure learning algorithm using the strategy of two-stage searches

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INTELLIGENT DATA ANALYSIS 2020年第5期24卷 1087-1106页

作者： Guo, Huiping Li, Hongru Northeastern Univ Informat Sci & Engn POB 13511 St 3Wenhua Rd Shenyang 110819 Liaoning Peoples R China

It is important for Bayesian network (BN) structure learning, a NP-problem, to improve the accuracy and hybrid algorithms are a kind of effective structure learning algorithms at present. Most hybrid algorithms adopt the strategy of one heuristic search and can be divided into two groups: one heuristic search based on initial BN skeleton and one heuristic search based on initial solutions. The former often fails to guarantee globality of the optimal structure and the latter fails to get the optimal solution because of large search space. In this paper, an efficient hybrid algorithm is proposed with the strategy of two-stage searches. For first-stage search, it firstly determines the local search space based on Maximal Information Coefficient by introducing penalty factors p(1), p(2), then searches the local space by binary particle swarm optimization. For second-stage search, an efficient ADR (the abbreviation of Add, Delete, Reverse) algorithm based on three basic operators is designed to extend the local space to the whole space. Experiment results show that the proposed algorithm can obtain better performance of BN structure learning.

关键词： Bayesian network structure learning hybrid algorithms penalty factors binary particle swarm optimization algorithm ADR algorithm

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Fault location of distribution networks based on multi-source information

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Global Energy Interconnection 2020年第1期3卷 77-85页

作者： Wenbo Li Jianjun Su Xin Wang Jiamei Li Qian Ai State Grid Shandong Electric Power Research Institute No.2000 Wangyue StreetJinan 250000P.R.China State Grid Shandong Electric Power Company No.150 Jinger StreetJinan 250000P.R.China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityNo.800 Dongchuan StreetShanghai 200240P.R.China

In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or ***,this study considers the influence of the distributed generations(DGs)for the distribution *** takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution *** example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.

关键词： Internet of Things Multi-source information D-S evidence theory binary particle swarm optimization algorithm Fault tolerance

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binary particle swarm optimization algorithm for Control of Single-Phase Full-Bridge Inverter

Binary Particle Swarm Optimization Algorithm for Control of ...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Wu Haitao Sun Yize Peng Lele Donghua Univ Sch Mech Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9781424448135

Control of inverter is a complicated optimization problem. It is a difficult task to find the optimum switching law by conventional techniques. The binary particle swarm optimization algorithm (BPSO) is characterized by its speediness, accuracy, efficiency, so it is well suited for online implementation. In this paper, BPSO is applied to solve optimum switching law of inverter which is regarded as a typical combination optimization problem. Simulation results show that harmonics of output current waveform is eliminated while switching loss of inverter is reduced. Simulation results indicate that binary particle swarm optimization algorithm is effective for the control of inverter.

关键词： Single-Phase Full-Bridge Inverter binary particle swarm optimization algorithm multiple objective optimizations switching loss harmonics

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