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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2025年 80卷

作者： Alrashed, Mohammed M. Elnaggar, Mohamed F. Flah, Aymen El-Bayeh, Claude Ziad Prince Sattam bin Abdulaziz Univ Coll Engn Dept Elect Engn Al Kharj Saudi Arabia Univ Gabes Natl Engn Sch Gabes Gabes 6072 Tunisia Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Jadara Univ Jadara Univ Res Ctr Irbid Jordan Univ Doha Sci & Technol Coll Engn Technol Doha Qatar

Accurate estimation of the parameters and speed of Permanent Magnet Synchronous Motors (PMSMs) is crucial for achieving optimal performance in control applications. Traditional methods, such as the Model Reference Adaptive System (MRAS) rely on manually tuned Proportional-Integral (PI) controllers, leading to suboptimal results due to fixed tuning parameters that do not adapt to varying operating conditions. This limitation affects the precision of parameter identification, leading to potential inefficiencies in motor control. This paper proposes an intelligent online estimation method that leverages Popov hyperstability theory and the bacterial foraging optimization (BFO) algorithm to address this issue. The proposed approach simultaneously estimates three key PMSM parameters - stator resistance, inductance, and permanent magnet flux - along with the actual motor speed. Unlike conventional methods, an online BFO-based tuning algorithm is integrated into the MRAS framework, allowing adaptive and optimal adjustment of controller parameters in real time. Extensive practical evaluations demonstrate that the proposed method significantly improves estimation accuracy and adaptability compared to traditional approaches. The results confirm its effectiveness in enhancing PMSM control performance, making it a promising solution for high-precision motor applications. Experimental results demonstrate a 12% improvement in estimation precision compared to traditional manual tuning methods.

关键词： PMSM MRAS Parameters estimation bacterial foraging optimization Speed variation

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bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

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APPLIED MATHEMATICS AND COMPUTATION 2015年 271卷 43-51页

作者： Alanis, Alma Y. Arana-Daniel, Nancy Lopez-Franco, Carlos Univ Guadalajara CUCEI Zapopan 45080 Jalisco Mexico

This paper deals with design parameter selection of a discrete-time neural second order sliding mode controller for unknown nonlinear systems, based on bacterial foraging optimization. First, a neural identifier is proposed in order to obtain a mathematical model for the unknown discrete-time nonlinear systems, then a novel second order sliding mode controller is proposed. Finally, both, the neural identifier and the controller are optimized using bacterial foraging algorithm. In order to illustrate the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator. (C) 2015 Elsevier Inc. All rights reserved.

关键词： bacterial foraging optimization Discrete-time second order sliding Mode controller Unknown nonlinear systems Discrete-time nonlinear systems Recurrent neural networks

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bacterial foraging optimization technique to calculate resonant frequency of rectangular microstrip antenna

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2008年第4期18卷 383-388页

作者： Gollapudi, Sastry V. R. S. Pattnaik, Shyam S. Bajpai, O. P. Devi, Swapna Sagar, Ch. Vidya Pradyumna, Patra K. Bakwad, K. M. NITTTR Dept ETV & ECE Chandigarh 160019 India Kurushektra Univ UIET Kurushektra 136119 India

Resonant frequency of rectangular microstrip antenna having various substrate thicknesses are generally calculated using the standard expressions presented in literatures. But these equations suffer from errors when compared with the experimental values due to Quasi-TEM propagation in the microstrip structure and fringing field. A number of researchers used soft computing approach such as neural networks and genetic algorithm on those equations to minimize the error for better accuracy. bacterial foraging, an evolutionary optimization technique conceived in recent times, has many advantages over genetic algorithm and is yet to be applied on the design of microstrip patch antenna. In this article, a novel technique is developed to apply bacterial foraging optimization technique in conjunction with the expressions developed to calculate accurately the resonant frequency of rectangular microstrip antenna of any dimension and of any substrate thickness. Further, bacterial foraging is applied to the calculatation of the feed point of microstrip antenna. The technique developed in this article can be a generalized soft computing tool to calculate resonant frequency of rectangular microstrip antenna. Similarly, the idea of the article can be used for calculating the various parameters of microstrip antennas of different structure and geometry. (C) 2008 Wiley Periodicals, Inc.

关键词： bacterial foraging optimization resonant frequency feed point microstrip antenna

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bacterial foraging optimization algorithm based on normal cloud model for forward kinematics of a 4-DOF parallel manipulator

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FERROELECTRICS 2022年第1期594卷 175-188页

作者： Wu Shenli Liang Xiaoming Liu Ling Yao Zimeng Xian Univ Sch Mech & Mat Engn Xian Peoples R China Shaanxi Key Lab Surface Engn & Remfg Xian Peoples R China Xian Key Lab Intelligent Addit Mfg Technol Xian Peoples R China

For the 4-SPU parallel mechanism, the complex multivariate nonlinear equation of the forward kinematics solution is transformed into a minimization problem. This article proposes a new bacterial foraging optimization algorithm based on normal cloud model to obtain the solution of the forward kinematics. Adaptive dynamic adjustment of walking step size of normal forward cloud generator is adopted to improve the fixed step size. Y conditional cloud generator is used to dynamically adjust the migration probability. The simulation results show that new algorithm is an effective method to solve the forward solution optimization with high precision and high speed.

关键词： Parallel manipulator forward kinematics bacterial foraging optimization normal cloud model

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bacterial foraging optimization of Speech Coder for Software Defined Radio Application

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期40卷 2625-2635页

作者： Gunjal, Sheetal D. Raut, Rajeshree D. Wagh, Abhay Amrutvahini Coll Engn Sangamner MS India GCOEN Nagpur MS India Tech Educ Mumbai MS India

The paper presents integration of Discrete Wavelet Cosine Transform technique and bacterial foraging Algorithm (BFO) for the development and optimization of speech coder. It is depicted how by filtering the limited number of high energy components of transformed coefficients with parallel programming can maintain the speech signal quality in coding over wide range of bit rates. The performance of existing and proposed speech coding technique attributes such as compression ratio, coding delay, computational complexity and quality of reconstructed speech is examined for multiple bit rates and compared with other existing speech coding techniques in Matlab environment. The result shows improvement in performance with respect to all attributes at the cost of increase in complexity.

关键词： Speech coder attributes Discrete Wavelet Cosine Transform bacterial foraging optimization Software Defined Radio

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bacterial foraging optimization using novel chemotaxis and conjugation strategies

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INFORMATION SCIENCES 2016年 363卷 72-95页

作者： Yang, Cuicui Ji, Junzhong Liu, Jiming Yin, Baocai Beijing Univ Technol Beijing Municipal Key Lab Multimedia & Intelligen Coll Comp Sci & Technol Beijing Peoples R China Hong Kong Baptist Univ Dept Comp Sci Kowloon Hong Kong Peoples R China

bacterial foraging optimization (BFO) has attracted much attention and been widely applied in a variety of scientific and engineering applications since its inception. However, the fixed step size and a lack of information communication between bacterial individuals during the optimization process have significant impacts on the performance of BFO To address these issues on real-parameter single objective optimization problems, this paper proposes a new bacterial foraging optimizer using new designed chemotaxis and conjugation strategies (BFO-CC). Via the new chemotaxis mechanism, each bacterium randomly selects a standard-basis-vector direction for swimming or tumbling;this approach may obviate calculating a random unit vector and could effectively get rid of interfering with each other between different dimensions. At the same time, the step size of each bacterium is adaptively adjusted based on the evolutionary generations and the information of the globally best individual, which readily makes the algorithm keep a better balance between a local search and global search. Moreover, the new designed conjugation operator is employed to exchange information between bacterial individuals;this feature can significantly improve convergence. The performance of the BFO-CC algorithm was comprehensively evaluated by comparing it with several other competitive algorithms (based on swarm intelligence) on both benchmark functions and real-world problems. Our experimental results demonstrated excellent performance of BFO-CC in terms of solution quality and computational efficiency. (C) 2016 Elsevier Inc. All rights reserved.

关键词： bacterial foraging optimization Nonuniform step size Standard basis vector Conjugation

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bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy

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APPLIED INTELLIGENCE 2019年第4期49卷 1283-1305页

作者： Pang, Bao Song, Yong Zhang, Chengjin Wang, Hongling Yang, Runtao Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Shandong Univ Weihai Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

bacterial foraging optimization (BFO), a biological-inspired optimization algorithm, has been applied in various fields, such as complex function optimization, robot path planning. However, there still exist several insufficiencies in BFO algorithm due to the fixed chemotaxis step-size, the less-efficient search direction for tumbling and the swarming strategy with lower convergence rate. In order to deal with these issues, based on the Levy flight step-size and particle swarm optimization (PSO) operator, this paper proposes the improved BFO algorithm (LPBFO). To reduce the mutual interference among different dimensions, each bacterium selects one dimension for tumbling randomly during the chemotactic process in LPBFO. The step-size of each bacterium is determined by the stochastic flight lengths of the improved Levy flight which can generate small step-size with high frequency and big step-size occasionally;moreover, the stochastic step-size is also reduced adaptively based on the evolutionary generations, which makes the bacteria transform from global search to local search. Furthermore, inspired by the social information term in PSO, this paper employed the global best solution to improve the swarming performance. Several experiments on benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. Experimental results show that the proposed algorithm achieves noticeable improvement compared with other competitive algorithms.

关键词： bacterial foraging optimization Levy flight Particle swarm optimization Chemotaxis step-size Self-regulation

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bacterial foraging optimization Approach to Portfolio optimization

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COMPUTATIONAL ECONOMICS 2013年第4期42卷 453-470页

作者： Kao, Yucheng Cheng, Hsiu-Tzu Tatung Univ Dept Informat Management Taipei 104 Taiwan

In this paper we propose a heuristic approach based on bacterial foraging optimization (BFO) in order to find the efficient frontier associated with the portfolio optimization (PO) problem. The PO model with cardinality and bounding constraints is a mixed quadratic and integer programming problem for which no exact algorithms can solve in an efficient way. Consequently, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of a BFO algorithm in solving the PO problem. BFO is a new swarm intelligence technique that has been successfully applied to several real world problems. Through three operations, chemotaxis, reproduction, and elimination-dispersal, the proposed BFO algorithm can effectively solve a PO problem. The performance of the proposed approach was evaluated in computational tests on five benchmark data sets, and the results were compared to those obtained from existing heuristic algorithms. The proposed BFO algorithm is found to be superior to previous heuristic algorithms in terms of solution quality and time.

关键词： bacterial foraging optimization Swarm intelligence Portfolio optimization Efficient frontier

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bacterial foraging optimization Combined with Relevance Vector Machine with an Improved Kernel for Pressure Fluctuation of Hydroelectric Units

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JOURNAL OF COMPUTERS 2013年第5期8卷 1273-1278页

作者： Wang, Liying Yang, Shaopu Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Hebei Univ Engn Sch Waterpower Handan 056038 Peoples R China Shijiazhuang Tiedao Univ Coll Mech Engn Shijiazhuang 050043 Hebei Peoples R China

The optimization of kernel parameters is an important step in the application of the Relevance Vector Machine (RVM) for many real- world problems. In this paper, firstly we have developed an improved anisotropic Gaussian kernel as the kernel function of the RVM model, whose parameters are optimized by bacterial foraging optimization (BFO). Then the proposed method is applied to describing the pressure fluctuation characteristics of the draft tube of hydroelectric units of a hydropower station, through the comparison, the simulation results show the parameters of the improved anisotropic Gaussian kernel are well optimized using the BFO, and the acquired RVM model can precisely describe the pressure fluctuation characteristics of the draft tube, and the less training samples are required to establish the accurate RVM model implying that it is more sparse than its counterpart.

关键词： bacterial foraging optimization Relevance Vector Machine Hydroelectric units Pressure Fluctuation

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bacterial foraging optimization for VLSI Fragments Placement 2nd

Bacterial Foraging Optimization for VLSI Fragments Placement

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2nd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Zaporozhets, Dmitry Zaruba, Daria Southern Fed Univ Rostov Na Donu Russia

ISBN: (纸本)9783319683218

The paper deals with one of the most significant problem of computer design - a VLSI placement problem. The authors prove a relevance of new heuristic methods for the given problem. The VLSI placement problem is formulated and an optimization criterion is suggested. As an optimization method there is suggested an algorithm based on bacterial colony behavior in nature according with environment conditions. On the basis of these observations it is developed a new bioinspired algorithm for the VLSI placement problem. There are conducted series of computational experiments for comparison of the developed algorithm with known VLSI placement algorithms on the basis of IBM benchmarks. Obtained results are represented in a table. Conducted researches shown the efficiency of the suggested approach better then 8-10% on average.

关键词： VLSI optimization Bioinspired algorithms bacterial foraging optimization IBM benchmarks

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