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Analysis influence factors and forecast energy-related CO2emissions: evidence from Hebei

ENVIRONMENTAL MONITORING AND ASSESSMENT 2020年第10期192卷 665-665页

作者： Sun, Wei Zhang, Junjian North China Elect Power Univ Dept Econ & Management Baoding 071000 Peoples R China

With economic development and the acceleration of urbanization, China's energy demand has gradually increased and brought a lot of energy-related CO(2)emissions. Energy-related CO(2)emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO(2)and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO(2)emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO(2)emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO(2)emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO(2)emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions.

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An ant approach to define product families and remanufacturing cells

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IFAC-PapersOnLine 2022年第10期55卷 73-78页

作者： Camilo Mejia-Moncayo Alix E. Rojas Jean-Pierre Kenne Lucas A. Hof Ećole de Technologie Supérieure Montréal Canada Universidad Ean Bogotá Colombia

Cellular manufacturing has the potential to reduce the complexity of a manufacturing system, decoupling a complex factory in mini-factories or cells. This concept has an immense potential to be implemented in remanufacturing, which is a promising circular economy strategy which allows to recover the value added to used products. A remanufacturing system is complex, and this creates a barrier to its implementation. This study introduces an ant-based algorithm which addresses the formation of product/part families and remanufacturing cells. A novel approach used in this study mimics ant behaviour by using an artificial pheromone that reinforces similarities between machines and parts, allowing the definition of cells and families with superior performance over Genetic algorithms (GA) and bacterial foraging optimization algorithm (BFOA) in the solution of benchmark problems.

关键词： Ant Colony optimization Genetic algorithm bacterial foraging optimization algorithm Cell formation Remanufacturing

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Bio-inspired hybrid BFOA-PSO algorithm-based reactive power controller in a standalone wind-diesel power system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第3期31卷

作者： Wagle, Raju Sharma, Pawan Sharma, Charu Gjengedal, Terje Pradhan, Chittaranjan UiT Arctic Univ Norway Dept Elect Engn Narvik Norway

With an increase in the penetration of renewable energy sources such as wind into the power systems, the operation and control of voltage/reactive power have become more complicated and challenging than ever. As a result, the reactive power imbalance between reactive power generation and demand instigates a reduction in system voltage stability. To deal with the aforesaid scenarios, automatic voltage regulator (AVR) and static synchronous compensator (STATCOM) are incorporated to curtail the voltage deviations in a standalone wind-diesel power system. In this article, a hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithm is proposed for optimizing the PI controller parameters of AVR and STATCOM to further improve the system voltage/reactive power performance. Additionally, H-infinity-loop shaping technique is designed to analyze the performance indexes (ie, robustness and stability) of the presented controller with the aim of handling the unstructured uncertainties from generation and loading situation. In order to present the efficiency of the proposed controllers, the performance of the hBFOA-PSO controller is compared with the performance of the BFOA, PSO, and modified grey wolf optimization (MGWO)-based PI controllers for the same wind-diesel system. The dynamic responses of the wind-diesel system for different disturbance cases have been investigated in the MATLAB/SIMULINK environment.

关键词： bacterial foraging optimization algorithm hybrid BFOA‐ PSO PI controller PSO wind‐ diesel standalone power system

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An Efcient Genetic Hybrid PAPR Technique for 5G Waveforms

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Computers, Materials & Continua 2021年第6期 3283-3292页

作者： Arun Kumar Mahmoud A.Albreem Mohammed H.Alsharif Abu Jahid Peerapong Uthansakul Jamel Nebhen Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Department of Electronics and Communications Engineering A’Sharqiyah UniversityIbra400Oman Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversityGwangjin-guSeoul05006Korea Department of Electrical&Computer Engineering University of OttawaOttawaON K1N 6N5Canada School of Telecommunication Engineering Suranaree University of TechnologyNakhon RatchasimaThailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Non-orthogonal multiple access(NOMA)is a strong contender multicarrier waveform technique for the fth generation(5G)communication *** high peak-to-average power ratio(PAPR)is a serious concern in designing the NOMA ***,the arrangement of NOMA is different from the orthogonal frequency division ***,traditional reduction methods cannot be applied to NOMA.A partial transmission sequence(PTS)is commonly utilized to minimize the PAPR of the transmitting NOMA *** choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA *** this study,an efcient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm(BFOA)for PTS(BFOA-PTS).The PAPR minimization is accomplished in a two-stage *** the initial stage,PTS is applied to the NOMA signal,resulting in the partition of the NOMA signal into an act of *** the second stage,the best phase factor is generated using *** performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional *** simulation outcomes reveal that the BFOA-PTS efciently optimizes the PAPR performance with inconsequential *** proposed method can signicantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.

关键词： Wireless networks 5G non-orthogonal multiple access peak to average power ratio partial transmission sequence bacterial foraging optimization algorithm

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Research on Control of A New Quasi-Z Source Photovoltaic Grid-Connected Inverter Based on Power Feedforward and Optimized PCI of bacterial foraging

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淡江理工学刊 2021年第4期24卷 595-609页

作者： Tao Hou Yuhu Chen Yannan Chen Caiwen Bao

Aiming at the low power level of the two-level Z-source inverter, the current and voltage harmonic distortion rate is high, the output power quality is low, The diode Neutral Point Clamp (NPC) three-level Z source inverter has insufficient boost capacity, and the capacitor voltage stress is low, the Z source network of the three-level inverter is improved and applied to photovoltaic grid-connected. In order to speed up the dynamic response speed of the quasi-Z-source photovoltaic grid-connected system, and for the quasi-Z-source photovoltaic grid-connected system with the existing current inner loop control output harmonic distortion rate is high, and the steady-state error is large. the power feedforward bacterial foraging proportional complex integral (BFOA-PCI) control method is put foreward. The system about this method reduces the steady-state error through the PCI controller, and adds the bacterial foraging optimization proportional coefficient K_P and integral coefficient K_i, makes K_P and K_i any the optimal conditions can be achieved, and introduces the output power feedforward of the photovoltaic array in the voltage outer loop, and its output is used as the reference value of the current inner loop. The method speeds up the system's response changes to the external environment, reduces the harmonic distortion rate, improves steady-state accuracy and the output power quality.

关键词： Boost Capacitor Voltage Stress Power Feedforward bacterial foraging optimization algorithm PCI

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Designing of energy efficient stable clustering protocols based on BFOA for WSNs

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第2期10卷 681-700页

作者： Gupta, Prateek Sharma, Ajay K. Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India Natl Inst Technol Dept Comp Sci & Engn New Delhi India

Efficient clustering method can competently scale down the energy consumption of sensor nodes (SNs) in wireless sensor networks (WSNs). Selection of the best-suited SNs for the role of cluster heads (CHs) can lead to effective clustering process. In past few decades, a number of clustering protocols have been designed to handle these issues in distributed WSNs. However, most of these employed estimation/randomized algorithms for CH selection due to lack of globalized energy awareness problem in distributed WSNs. This paper resolves the problem by using proposed Modified Intelligent CH election based on bacterial foraging optimization algorithm (M-ICHB), which searches actual higher residual energy SNs for CH selection in distributed WSNs. M-ICHB algorithm does not require any estimation/randomized algorithms during CH selection process, which resolves the issue of energy unawareness problem in the WSN. Moreover in general, most of the existing clustering algorithms have been designed either for homogeneous or heterogeneous WSNs. However in contrary, proposed M-ICHB algorithm is designed for both homogeneous as well as heterogeneous WSNs in this paper. Furthermore, in many critical applications i.e., military surveillance, traffic management, natural disaster forecasting and structural health monitoring;reliability of data from each SN is the most crucial aspect. In this prospect, elongated stability region (from the network initiation till first node dies) of the network is the prime necessity. For this, we have applied proposed M-ICHB algorithm on conventional stability based clustering protocols i.e., LEACH, SEP and DEEC and proposed M-ICHB based stable protocols viz MILEACH, MIrLEACH, MISEP and MIDEEC protocols. Simulation results confirm that proposed MILEACH, MIrLEACH, MISEP and MIDEEC protocols are capable in searching actual higher residual energy nodes for CH selection without using any estimation/randomized algorithm, while maintaining distributive nature o

关键词： Wireless sensor networks Clustering Network lifetime Stability region ICHB algorithm bacterial foraging optimization algorithm

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A Coupling Approach With GSO-BFOA for Many-Objective optimization

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IEEE ACCESS 2019年 7卷 120248-120261页

作者： Zhang, Jiangjiang Cui, Zhihua Wang, Yechuang Wang, Hui Cai, Xingjuan Chen, Jinjun Li, Wuzhao Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia Jiaxing Vocat Tech Coll Jiaxing 314001 Peoples R China

Glowworm swarm optimization (GSO) and bacterial foraging optimization algorithm (BFOA) are two popular swarm intelligence optimization algorithms (SIOAs). However, both GSO and BFOA show some difficulties when solving many-objective optimization problems (MaOPs). To challenge MaOPs, a coupling approach based on GSO and BFOA is proposed in this paper. To implement the coupling method, an external archive is established to save the best solutions found so far. The internal populations in GSO and BFOA can exchange the search information with the external archive in the evolutionary process. Simulation experiments are verified on two benchmark sets (DTLZ and WFG) with 3 to 15 objectives. The performance of our approach is compared with five other famous algorithms including NSGA-III, KnEA, MOEA/D-DE, GrEA and HypE. Results prove the effectiveness of our approach.

关键词： Swarm intelligence glowworm swarm optimization bacterial foraging optimization algorithm external archive many-objective optimization

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Demand Estimation of Water Resources based on Coupling algorithm 31

Demand Estimation of Water Resources based on Coupling Algor...

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第31届中国控制与决策会议

作者： Jiangjiang Zhang Zhuanghua Zhu Yu Chang Di Wu Lei Du Zhihua Cui Complex System and Computational Intelligence Laboratory TaiYuan University of Science and Technology Shanxi Finance &Taxation College

ISBN: (纸本)9781728101071;9781728101064

Demand estimation of water resources is an important basic content in the process of urban water resources planning. To predict water resource demand accurately, a coupling algorithm based on bacterial foraging optimization algorithm(BFOA) and glowworm swarm optimization(GSO) is employed to estimate the demand of water resources in this paper. The historical data(2003-2015) is divided into two parts for convenience in the simulation experiments, one(2003-2012) is trained to gain the weighting factors of the estimation and the other(2013-2015) is used for predicting and evaluating water resources. In addition, the simulation results show that the coupling algorithm achieves a higher accuracy compare with BFOA and GSO on prediction.

关键词： water resources bacterial foraging optimization algorithm glowworm swarm optimization

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Flexible Job-Shop Scheduling Problem With Sequencing Flexibility: Mathematical Models And Solution algorithms

Flexible Job-Shop Scheduling Problem With Sequencing Flexibi...

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作者： Alejandro Vital Vital Soto University of Windsor

学位级别：博士

Marketing strategists usually advocate increased product variety to attend better market demand. Furthermore, companies increasingly acquire more advanced manufacturing systems to take care of the increased product mix. Manufacturing resources with different capabilities give a competitive advantage to the industry. Proper management of the current productions resources is crucial for a thriving industry. Flexible job shop scheduling problem (FJSP) is an extension of the classical Job-shop scheduling problem (JSP) where operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled FJSP with sequencing flexibility (FJSPS), is studied in this work. The extension considers precedence between the operations in the form of a directed acyclic graph instead of sequential order. In this work, a mixed integer programming (MILP) formulation is presented. A single objective formulation to minimize the weighted tardiness for the FJSP with sequencing flexibility is proposed. A different objective to minimize makespan is also considered. Due to the NP-hardness of the problem, a novel hybrid bacterial foraging optimization algorithm (HBFOA) is developed to tackle the FJSP with sequencing flexibility. It is inspired by the behaviour of the E. coli bacteria. It mimics the process to seek for food. The HBFOA is enhanced with simulated annealing (SA). The HBFOA has been packaged in the form of a decision support system (DSS). A case study of a small and medium-sized enterprise (SME) manufacturing industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments with instances provided by the literature are considered. The results demonstrate that the HBFOA outperformed the classical dispatching rules and the best integer solution of MILP when minimizing the weighted tardiness and offered comparable results for the makespan instances. In this dissertation, another critical aspect has been studied. In the indu

关键词： Alternative process plans bacterial foraging optimization algorithm Dual-resource problem Flexible job-shop scheduling problem Multi-objective optimization Sequencing flexibility

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Design of a Supplementary Controller for Power System Stabilizer Using bacterial foraging optimization algorithm

Design of a Supplementary Controller for Power System Stabil...

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1st International Conference on Computational Intelligence and Information Technology (CIIT 2011)

作者： Kiani, Mohammad Soloklo, Hasan Nasiri Mohammadi, M. Ali Farsangi, Malihe M. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9783642257339

Power system stabilizers (PSSs) are the most efficient device to damp the power system oscillation. Recently designing of PSSs by heuristic algorithms is the conventional method for damping the power system oscillation. This work proposes a bacterial foraging optimization algorithm (BFOA) to determine the PSS parameters to damp the power system inter area oscillation. The results are presented on a 2 area 4 machine system to feasibility of the proposed method. To show the effectiveness of the designed controller, a three phase fault is applied at a bus. The simulation results show that the designed controller by BFOA algorithm performs better than well known genetic algorithm (GA) in damping the power oscillation.

关键词： bacterial foraging optimization algorithm Power System Stabilizer supplementary control Genetic algorithm

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