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Optimal selection of cloud manufacturing resources based on bacteria foraging optimization

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第1-2期37卷 165-182页

作者： Hu, Yanjuan Pan, Leiting Lv, Wenjun Wang, Zhanli Changchun Univ Technol Sch Mechatron Engn Changchun Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

As a service-oriented manufacturing paradigm, cloud manufacturing (CMfg) combines the computing resources the manufacturing resources respectively from the Internet and enterprises into a huge shared resource pool for network-based distribution of manufacturing resources and manufacturing tasks. In order to reduce the number and complexity of resource pools in CMfg, it is necessary to simplify the process, save manufacturing resources, shorten the production time and improve the productivity of enterprises. Therefore, a method for the optimal selection of cloud Manufacturing resources based on bacterial foraging optimization was proposed in this paper. First, a scheme evaluation system based on the evaluation factors of production cost, production time and processing quality was established, and a mathematical model of resource optimization selection was built. Secondly, a combination of Analytic Hierarchy Process (AHP) and entropy weight method was used to obtain the weights of different evaluation indicators, and a bacterial foraging optimization algorithm (BFO) was used to optimize the scheme. Finally, an example study of the method was conducted with the NGW51 reducer as an example. By comparing with existing methods, the experimental results verify the advantages of BFO in CMfg resource optimization selection.

关键词： Cloud manufacturing resource optimization evaluation indicators bacterial foraging optimization algorithm analytic hierarchy process entropy weight method

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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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Fuzzy Cognitive Map to Classify Plantar Foot Alterations

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IEEE LATIN AMERICA TRANSACTIONS 2021年第7期20卷 1992-2000页

作者： Andres Ramirez-Bautista, Julian Chaparro-Cardenas, Silvia L. Hernandez-Zavala, Antonio Magdalena Gallegos-Torres, Ruth Zequera, Martha Tovar-Barrera, Yosabad Pradilla-Gomez, Juan M. Adalberto Huerta-Ruelas, Jorge Fdn Univ San Gil UNISANGIL Res Dept Km2 Via San Gil Charala Santander Colombia Inst Politecn Nacl Ctr Invest Ciencia Aplicada & Tecnol Avanzada Av Cerro Blanco 141 Queretaro Mexico Univ Autonoma Queretaro Dept Nursing Queretaro 76010 Mexico Pontificia Univ Javeriana Elect Dept BASPI FootLaB Cra 7 40-62 Bogota Colombia Yosafisio Clin Queretaro Queretaro Mexico Pontificia Univ Javeriana Sch Med Cra 7 40-62 Bogota Colombia

The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intelligence techniques to obtain accurate and easy-to-use systems. This paper presents the application of a Fuzzy Cognitive Map (FCM) formulation, for knowledge extraction in the classification of human plantar foot alterations, with a relatively small and transparent model. The FCM is trained using the bacterial Search optimization algorithm (BFOA). One hundred and twenty-five volunteer subjects (aged 20-68 years) participated in the study. Classification of the foot into normal (n=31), flat (n=32), cavus type III (n=31) and cavus type IV (n=31) to train the system was performed by specialized physicians. The test was performed by walking on a FreeMed platform. The proposed method shows an accuracy rate of about 89% in the classification task and allows extracting information related to the important factors that the system considers to make a decision.

关键词： Foot Microorganisms optimization Medical services Fuzzy cognitive maps Behavioral sciences Biological system modeling Clinical decision support systems bacterial foraging optimization algorithm fuzzy cognitive maps optimization algorithms plantar data analysis

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Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

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NEURAL COMPUTING & APPLICATIONS 2018年第12期29卷 1411-1434页

作者： Sivarani, T. S. Jawhar, S. Joseph Kumar, C. Agees Kumar, K. Prem Arunachala Coll Engn Women Dept EEE Vellichanthai 629203 Tamil Nadu India Rajalakshmi Engn Coll Dept EEE Chennai Tamil Nadu India

In this paper, a novel adaptive neuro-fuzzy inference system (ANFIS)-based control technique optimized by bacterial foraging optimization algorithm for speed control of matrix converter (MC)-fed brushless direct current (BLDC) motor is presented. ANFIS is considered to be one of the most promising technologies for control of electrical drives fed by MC. Optimizing the training parameters of ANFIS, to improve its performance, is still being considered by several researchers recently. Parameters of the online ANFIS controller such as learning rate (eta), forgetting factor (lambda) and steepest descent momentum constant (alpha) are optimized by using the proposed algorithm. For the purpose of comparison, proportional integral derivative controller, fuzzy logic controller, PSO-ANFIS and BAT-ANFIS are considered. Set point tracking performances of the proposed system are carried out at various operating points for an industrial BLDC motor operating at a maximum rated speed of 380 rpm and torque of 6.4 N m. Time domain specifications such as rise time, settling time, peak time, steady-state error and peak overshoot in the presence and absence of load torque disturbances are presented. Time integral performance measures such as integral square error, integral absolute error, and integral time multiplied absolute error are analyzed for various operating conditions. Speed fluctuation in the output of BLDC motor is dependent on the source current harmonics of the inverter/converter. To illustrate this, total harmonic distortion (THD) analysis is carried out for the existing PWM inverter and the proposed MC, and it is proved that MC results in reduced THD, as compared to PWM inverter. Simulation results confirm that the proposed controller outperforms the other existing control techniques under various set speed and torque conditions. Statistical analysis is effectively carried out to prove the effectiveness of the proposed controller. Experimental analysis is performed to val

关键词： Brushless direct current motor Fuzzy logic controller bacterial foraging optimization algorithm Adaptive neuro-fuzzy inference system Particle swarm optimization Bat algorithm Matrix converter Total harmonic distortion Speed control Statistical measures

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

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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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Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第3期12卷 1027-1037页

作者： Anbarasi, S. Muralidharan, S. Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm optimization (PSO), bacterial foraging optimization algorithm (BFOA) and hybrid bacterial foraging Particle Swarm optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

关键词： bacterial foraging optimization algorithm Hybrid bacterial foraging particle swarm optimization algorithm Load frequency control Particle swarm optimization algorithm PID controller

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A hybrid MACO and BFOA algorithm for power loss minimization and total cost reduction in distribution systems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第1期25卷 337-351页

作者： Sankaramoorthy, Muthubalaji Veluchamy, Malathi Lathamathavan Engn Coll Dept Elect & Elect Engn Madurai Tamil Nadu India Anna Univ Dept Elect & Elect Engn Reg Campus Madurai Tamil Nadu India

This paper presents a multiobjective optimization methodology to optimally place a STATCOM in electric power distribution networks. The combination of multiobjective ant colony optimization (MACO) and the bacterial foraging optimization algorithm (BFOA) is proposed to minimize the power loss and total cost. The main intention of this analysis is to optimally place the STATCOM at multiple locations such as the transmission side, middle, and load side. Identifying the type and location of the STATCOM is a combinatorial optimization problem in power systems. In order to overcome this problem, the combination of hybrid MACO and BFOA algorithms is applied in this analysis to minimize the total cost and power loss. The total cost of the overall network is calculated by using system average interruption duration index and system average interruption frequency index metrics. Moreover, the BFOA is used in this paper to minimize the power loss during distribution, which is adequate in searching for the optimal solution. The problem of reducing power losses in distribution systems through the BFOA approach is described here for a 5-bus system. Test results of a 5-bus network show that the proposed MACO with BFOA method can efficiently ensure the power loss and total cost minimization.

关键词： Multi-objective ant colony optimization bacterial foraging optimization algorithm system average interruption duration index system average interruption frequency index STATCOM electric power distribution system reliability

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Design, implementation, control and optimization of single stage pilot scale reverse osmosis process

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WATER SCIENCE AND TECHNOLOGY 2021年第10-11期84卷 2923-2942页

作者： Guna, G. Prabhakaran, D. Thirumarimurugan, M. Coimbatore Inst Technol Dept Chem Engn Coimbatore Tamil Nadu India

The closed loop configuration of a RO control system is depicted. The controller FOPID takes the input and calculates the frequency input of the final control element. The final control element is variable frequency drive whose flow rate changes with the change in frequency. When the flow rate changes pH and the turbidity of the RO process changes which is measured using the feedback mechanism in this case the pH meter and the turbidity sensors are the feedback mechanisms.

关键词： bacterial foraging optimization algorithm multi input multi output particle swarm optimization reverse osmosis various industrial applications

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An Efficient Genetic Hybrid PAPR Technique for 5G Waveforms

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CMC-COMPUTERS MATERIALS & CONTINUA 2021年第3期67卷 3283-3292页

作者： Kumar, Arun Albreem, Mahmoud A. Alsharif, Mohammed H. Jahid, Abu Uthansakul, Peerapong Nebhen, Jamel JECRC Univ Dept Elect & Commun Engn Jaipur 303905 Rajasthan India ASharqiyah Univ Dept Elect & Commun Engn Ibra 400 Oman Sejong Univ Coll Elect & Informat Engn Dept Elect Engn Seoul 05006 South Korea Univ Ottawa Dept Elect & Comp Engn Ottawa ON K1N 6N5 Canada Suranaree Univ Technol Sch Telecommun Engn Nakhon Ratchasima Thailand Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj 11942 Saudi Arabia

Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in designing the NOMA waveform. However, the arrangement of NOMA is different from the orthogonal frequency division multiplexing. Thus, traditional reduction methods cannot be applied to NOMA. A partial transmission sequence (PTS) is commonly utilized to minimize the PAPR of the transmitting NOMA symbol. The 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 symbol. In this study, an efficient 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 process. In the initial stage, PTS is applied to the NOMA signal, resulting in the partition of the NOMA signal into an act of sub-blocks. In the second stage, the best phase factor is generated using BFOA. The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS. The simulation outcomes reveal that the BFOA-PTS efficiently optimizes the PAPR performance with inconsequential complexity. The proposed method can significantly 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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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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