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Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 3945-3961页

作者： Li, Weidong Fan, Jinsheng Lin, Zhenying Wang, Chisheng Zhang, Xuehai Duan, Jinlong Henan Univ Technol Minist Educ Key Lab Grain Informat Proc & Control Zhengzhou Peoples R China Henan Univ Technol Henan Key Lab Grain Photoelectr Detect & Control Zhengzhou Peoples R China Yellow River Engn Consulting Co Ltd Post Doctoral Programme Zhengzhou Peoples R China Shenzhen Univ Key Lab Geoenvironm Monitoring Great Bay Area MNR Shenzhen Peoples R China

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFISFCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance.

关键词： ANFIS ANN bacterial foraging optimization algorithm modeling suspended sediment

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Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Meng, Guanglei Cong, Zelin Li, Tingting Wang, Chenguang Zhou, Mingzhe Wang, Biao Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China Aviat Sci & Technol Key Lab Air Combat Syst Techno Shenyang 110136 Peoples R China

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining spec

关键词： Dynamic Bayesian networks Structural learning Swarm intelligence optimization algorithm bacterial foraging optimization algorithm

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optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2020年第3期24卷 171-181页

作者： Benmachiche, A. Makhlouf, A. Bouhadada, T. Chadli Bendjedid Univ Dept Comp Sci PB 73 El Tarf 36000 Algeria Badji Mokhtar Univ Dept Comp Sci PB 12 Annaba 23000 Algeria Badji Mokhtar Univ Lab LRI PB 12 Annaba 23000 Algeria

Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone ... Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.

关键词： Automatic speech recognition acoustic information bacterial foraging optimization algorithm BFOA/HMM Gaussian mixture densities Baum-Welch

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A Hybrid bacterial foraging optimization algorithm and a Radial Basic Function Network for Image Classification

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2017年第2期13卷 215-235页

作者： Amghar, Yasmina Teldja Fizazi, Hadria Univ Sci & Technol Oran Mohamed Boudiaf Dept Comp Sci Fac Math & Comp Sci Oran Algeria

foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

关键词： bacterial foraging optimization algorithm Hybrid Image Classification Radial Basic Function

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Demand Side Management Using bacterial foraging optimization algorithm 3rd

Demand Side Management Using Bacterial Foraging Optimization...

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3rd International Conference on Information System Design and Intelligent Applications (INDIA)

作者： Esther, B. Priya Krishna, K. Shivarama Kumar, K. Sathish Ravi, K. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9788132227557;9788132227533

Demand side management (DSM) is one of the most significant functions involved in the smart grid that provides an opportunity to the customers to carryout suitable decisions related to energy consumption, which assists the energy suppliers to decrease the peak load demand and to change the load profile. The existing demand side management strategies not only uses specific techniques and algorithms but it is restricted to small range of controllable loads. The proposed demand side management strategy uses load shifting technique to handle the large number of loads. bacterial foraging optimization algorithm (BFOA) is implemented to solve the minimization problem. Simulations were performed on smart grid which consists of different type of loads in residential, commercial and industrial areas respectively. The simulation results evaluates that proposed strategy attaining substantial savings as well as it reduces the peak load demand of the smart grid.

关键词： Smart grid Demand side management bacterial foraging optimization algorithm Load shifting

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Active Noise Control using bacterial foraging optimization algorithm

Active Noise Control using Bacterial Foraging Optimization A...

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IEEE 10th International Conference on Signal Processing

作者： Gholami-Boroujeny, Shiva Eshghi, Mohammad Shahid Beheshti Univ Elect & Comp Engn Fac Tehran Iran

ISBN: (纸本)9781424458981

This paper proposes a nonlinear ANC system in which a new evolutionary algorithm based on the foraging behavior of E. coli bacteria is used for adaptive nonlinear filter coefficients optimization, called BFA_ANC. While the standard LMS based nonlinear filters may converge to local minima, the evolutionary algorithms may handle this problem efficiently and converge to the global minima. In addition, this class of algorithms does not require the identification of the secondary paths. Computer simulations show the major improvements in final residual noise, as well as the processing time of the proposed ANC system in comparison to the other techniques in the literature.

关键词： active noise control evolutionary algorithm bacterial foraging optimization algorithm BFA_ANC

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An Adaptive bacterial foraging optimization algorithm Based on Chaos-Enhanced Non-elite Reverse Learning 15th

An Adaptive Bacterial Foraging Optimization Algorithm Based ...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Yong, Yibo Ma, Lianbo Gao, Yang Northeastern Univ Coll Software Boston MA 02115 USA Northeastern Univ Foshan Grad Sch Innovat Boston MA 02115 USA

ISBN: (纸本)9789819771806;9789819771813

bacterial foraging optimization algorithm (BFO) is a swarm intelligence-based optimization algorithm that has been widely applied in various fields. However, the classical BFO still suffers from two major limitations: The fixed chemotactic step-size makes it difficult to balance exploration and exploitation capabilities, and the non-elitist elimination strategy in the reproduction phase may lead the population to be trapped in local optima. To address the two limitations of the classical BFO, this paper presents an improved BFO with adaptive chemotactic step-size and chaos-enhanced non-elite reverse learning (CLBFO). In CLBFO, the chemotactic step employs an adaptive nonlinear dynamic step-size strategy. Each bacterial individual adaptively selects an appropriate chemotactic step-size at different stages of the optimization process, which effectively alleviates the low search efficiency and oscillation problems caused by the fixed step-size. The reproduction step improves the non-elite solutions based on non-elite reverse learning and incorporates a chaotic disturbance mechanism to enhance the convergence speed and effectively reduce the possibility of the population falling into local optima. The performance of the proposed algorithm was evaluated on the benchmark test suite and compared with that of other intelligent optimization algorithms. The comparisons demonstrated the effectiveness of the proposed algorithm in balancing exploration and exploitation, and reducing the risk of local convergence.

关键词： bacterial foraging optimization algorithm Adaptive chemotactic step-size Chaos-enhanced non-elite reverse learning

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A bacterial foraging optimization algorithm for User Interface Layout Design in Complex Human-computer Interaction System 24

A Bacterial Foraging Optimization Algorithm for User Interfa...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Li, Xinyan Cui, Yan Jin, Nianfu Su, Jin Pang, Fengying Gao, Ming China North Vehicle Res Inst Beijing 100072 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

ISBN: (纸本)9781728165974

The layout design of user interface (UI) is a key issue in making a better human-computer interaction system for complex system such as spacecraft or vehicles, which directly affects the information level, tactical performance and operational efficiency. Firstly, this study builds a model for the layout design of UI which concerns the importance and use frequency of the layout components. Then, an improved bacterial foraging optimization algorithm is proposed to solve the layout optimization problems, which include the process of chemotaxis, replicates and migration. The preliminary results showed the feasibility of the proposed method.

关键词： User Interface bacterial foraging optimization algorithm Layout optimization Problems

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Control Strategy for PQ Improvement in an Autonomous Microgrid Using bacterial foraging optimization algorithm

Control Strategy for PQ Improvement in an Autonomous Microgr...

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Chitra, N. Kumar, A. Senthil Priyadharshini, P. Shobana, K. M. SKP Engn Coll Dept Elect & Elect Engn Tiruvannamalai 606611 India

ISBN: (纸本)9788132221357;9788132221340

This paper bestows a global optimization algorithm-based optimal power control stratagem for an island microgrid. The foremost aim is to improve the power quality of the microgrid. The primary performance parameters that are considered are voltage regulation and frequency regulation, especially starting of island mode. An inner loop of current control and an outer loop of power control are combined to form the projected control strategy. bacterial foraging optimization algorithm (BFOA) is an intellectual search algorithm which is employed for self-tuning the control parameters. To validate the performance of the controllers, simulation is performed with the help of MATLAB/Simulink software.

关键词： bacterial foraging optimization algorithm Microgrid Distributed generation Voltage source inverter Hysteresis current control

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A Comparison Between Adaptive PID Controller and PID Controller with Derivative Path Filter Based on bacterial foraging optimization algorithm 2

A Comparison Between Adaptive PID Controller and PID Control...

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2nd International Conference on Energy, Power and Environment - Towards Smart Technology (ICEPE)

作者： Das, Navaneet Sengupta, Anindita IIEST Shibpur Dept Elect Engn Howrah W Bengal India

ISBN: (纸本)9781538647691

In this paper, design of conventional PID controller along with PID controller with derivative path filter both optimized by bacterial foraging optimization algorithm, for one unstable system and one stable system, is discussed. This algorithm is fairly new stochastic optimization algorithm and has found its way in quite some research works recently proving its effectiveness. Here the unstable system is 1-Stage Inverted Pendulum system which is essentially a nonlinear system and its linearized transfer function with angular position of rod as output is only considered. On the other hand a series RLC plant is taken as a stable system. Both plants are included to show the effectiveness of this scheme. The controllers designed by this scheme are quantitatively analyzed and compared with each other and the later clearly shows significant improvements in different performance indices. Relevant Matlab/Simulink simulation results showing this are included in support of this scheme.

关键词： bacterial foraging optimization algorithm BFO BFOA Classical PID PID PID with derivative path filter PID(N) 1-Stage Inverted Pendulum Inverted Pendulum Series RLC plant

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