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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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Optimal gait planning of bionic lower extremity exoskeleton based on elite mechanism bacterial foraging optimization algorithm

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FERROELECTRICS 2023年第1期610卷 1-18页

作者： Shenli, Wu Jing, Li Ling, Liu Yixuan, Li Xiaoming, Liang Zimeng, Yao Jun, Chen Jingqi, Pu Yingbo, Zhao 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

To improve the stability and smoothness of bionic lower extremity exoskeleton walking gait, the elitist mechanism was introduced to enhance the diversity and improve the search ability of the optimal solution. The simulation analyses showed that the improved bacterial foraging algorithm had higher convergence accuracy, and was not easy to fall into local extremum. The improved foraging optimization algorithm was used for the optimization of the bionic lower extremity exoskeleton gait zero moment point stability margin. The simulation results showed that the optimal gait planning method proposed can increase the stability margin and ensure the stable and smooth walking.

关键词： Lower extremity exoskeleton gait optimization bacterial foraging optimization algorithm elite mechanism

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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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Double Sided Optimal Bidding Strategy for Electricity Market Using Two Stage bacterial foraging optimization algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第3期17卷 1499-1508页

作者： Karri, Chandram Kiran, L. Uday Raju, S. Siva Naga BITS Pilani Dept Elect Engn KK Birla Goa Campus Sancoale Goa India JNT Univ Dept Elect Engn Kakinada India JNT Univ Kakinada India

In this article, bacterial foraging optimization (BFO) algorithm is developed for double sided optimal bidding strategy in an electricity market. Optimal bidding strategy is one of the important functions in the electricity market along with forecasting of the electricity price and the profit based unit commitment. The prime objective of generating company (Genco) and distribution company (Disco) is to maximize their profit when they participate in the bidding process. Two stages are involved in the proposed approach. In the first stage, the BFO algorithm has been used to maximize the probability density function (pdf). In the second stage the BFO algorithm is again applied to maximize the profit of the GENCO and DISCO. The Proposed algorithm is developed in MATLAB (Version, 2019) and tested on standard test case available in the literature. Also, the simulation results are presented and compared. It is noticed that the proposed method yields the best results in terms of profit.

关键词： Double sided optimal bidding strategy bacterial foraging optimization algorithm IEEE 30 Bus system Marginal clearing price

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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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Softmax Regression Based on bacterial foraging optimization algorithm with t-Distribution Parameters 11

Softmax Regression Based on Bacterial Foraging Optimization ...

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11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Qiao, Xinyi Yang, Zan Hua, Zilin Wang, Yingxuan Nai, Wei Li, Dan Xing, Yidan Tongji Zhejiang Coll Dept Accounting Jiaxing 314051 Zhejiang Peoples R China Tongji Zhejiang Coll Fac Sci Jiaxing 314051 Zhejiang Peoples R China Tongji Zhejiang Coll Dept Jewelry Jiaxing 314051 Zhejiang Peoples R China Tongji Zhejiang Coll Dept Elect & Informat Engn Jiaxing 314051 Zhejiang Peoples R China

ISBN: (纸本)9780738111353

Softmax regression is a supervised multi-class nonlinear classification algorithm in machine learning. Sometimes it is also used in regression problems. It is mainly used in transfer learning, knowledge distillation and meta learning in artificial intelligence. Softmax optimizer is gradient descent method or random gradient descent method, but its loss function is a multi peak, strong nonlinear function, and the optimal solution is only the local optimal solution rather than the global optimal solution, and depends on the gradient. So we use a swarm intelligence optimization algorithm which is independent of gradient and can find the global optimal solution to optimize This article will introduce the bacterial foraging optimization algorithm.

关键词： component softmax regression bacterial foraging optimization algorithm t-distribution

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Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility

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JOURNAL OF MANUFACTURING SYSTEMS 2020年 54卷 74-93页

作者： Vital-Soto, Alejandro Azab, Ahmed Baki, Mohammed Fazle Univ Windsor Fac Engn Prod & Operat Management Res Lab Windsor ON N9B 3P4 Canada Univ Windsor Odette Sch Business Windsor ON N9B 3P4 Canada

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP) in which operations can be performed by a set of candidate capable machines. An extended version of the FJSP, entitled sequencing flexibility, is studied in this work, which considers precedence between the operations in the form of a directed acyclic graph instead of a sequential order. In this work, a mixed integer linear programming (MILP) formulation is presented to minimize weighted tardiness for the FJSP with sequencing flexibility. Due to the NP-hardness of the problem, a novel biomimicry hybrid bacterial foraging optimization algorithm (HBFOA) is developed, which is inspired by the behavior of E. colt bacteria in its search for food. The developed HBFOA search method is hybridized with simulated annealing (SA). Additionally, the algorithm has been enhanced by a local search method based on the manipulation of critical operations. Classical dispatching rules have been employed to create the initial swarm of HBFOA, and a new dispatching rule named minimum number of operations has been devised. The developed approach has been packaged in the form of a decision support system (DSS) developed on top of Microsoft Excel-a tool most small and mid-range enterprises (SME) use heavily for planning. A case study with local industry is presented to validate the proposed HBFOA and MILP. Additional numerical experiments using literature benchmarks are further used for validation. 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.

关键词： Flexible job-shop scheduling problem Sequencing-flexibility bacterial foraging optimization algorithm Simulated annealing Weighted tardiness Decision support system

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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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Performance enhancement of SRM using smart bacterial foraging optimization algorithm based speed and current PID controllers

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 95卷 107398-107398页

作者： Manjula, A. Kalaivani, L. Gengaraj, M. Maheswari, R. V. Vimal, S. Kadry, Seifedine Mohamed Sathak Engn Coll Dept EEE Ramanathapuram Tamil Nadu India Natl Engn Coll Dept EEE Kovilpatti Tamil Nadu India Ramco Inst Technol Dept CSE Rajapalayam Tamil Nadu India Noroff Univ Coll Fac Appl Comp & Technol Kristiansand Lebanon

In modern era, the switched reluctance motors (SRM) are gaining attraction due to its inherent features such as robustness, low cost, simple, rugged structure, excellent fault tolerance and temperature withstanding capability. With these specialized advantages, the accurate and efficient energy analysis is still a challenging one since they operated at variable reluctance and oscillating excitation characteristics resulting in nonlinear characteristics. This paper focused on the energy improvement and performance analysis inclusive of smooth control in speed and minimized torque ripples of SRM by introducing the novel Bio-inspired methodology named bacterial foraging optimization algorithm (BFOA) for the selection of optimal parameters of the PID speed and current controllers. By minimizing the torque ripples, it increases the average torque which in turn increases the energy conversion. The performance of the SRM is measured in terms of speed, current, power and efficiency and BFOA efficiency has been verified with genetic algorithm (GA) and conventional PID controller using Euler forward approximation method. This paper discusses the modeling, controlling strategy using BFOA, GA and Euler forward approximation method and a comprehensive analysis of energy using optimized selection of controlling parameters. The performance evaluation objectives in this work, is to minimize Integral Square Error of both current and speed controller and also the reduction of torque ripples. The results obtained in this method are also compared with the Genetic algorithm and Euler forward approximation-based controllers. The proposed algorithm employs individual and social intelligences, which in turn search the responses between the local optima along with global optimums of the problem adaptively. The outmost dynamic response increased average torque and minimized current ripple can be obtained when the parameters of PID controllers are optimized using BFOA.

关键词： Switched Reluctance Motor Torque Minimization Power Equation Speed Control bacterial foraging optimization algorithm Genetic algorithm AI

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