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bacterial foraging algorithm with Varying Population for Optimal Power Flow

EvoWorkshops 2007

作者： Li, M. S. Tang, W. J. Tang, W. H. Wu, Q. H. Saunders, J. R. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England Univ Liverpool Sch Biol Sci Liverpool L69 3GJ Merseyside England

ISBN: (纸本)9783540718048

This paper proposes a novel optimization algorithm, bacterial foraging algorithm with Varying Population (BFAVP), to solve Optimal Power Flow (OPF) problems. Most of the conventional Evolutionary algorithms (EAs) are based on fixed population evaluation, which does not achieve the full potential of effective search. In this paper, a varying population algorithm is developed from the study of bacterial foraging behavior. This algorithm, for the first time, explores the underlying mechanisms of bacterial chemotaxis, quorum sensing and proliferation, etc., which have been successfully merged into the varying-population frame. The BFAVP algorithm has been applied to the OPF problem and it has been evaluated by simulation studies, which were undertaken on an IEEE 30-bus test system, in comparison with a Particle Swarm Optimizer (PSO) [1].

关键词： bacterial foraging algorithm Evolutionary algorithm Optimal power flow Varying population

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Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

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Journal of Systems Engineering and Electronics 2018年第1期29卷 166-175页

作者： LIU Lu SHAN Liang DAI Yuewei LIU Chenglin QI Zhidong School of Automation Nanjing University of Science and Technology Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population *** tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

关键词： bacterial foraging algorithm fractional-order quantum rotation gate proportion integration differentiation(PID) servo system

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Quantum-inspired bacterial foraging algorithm for parameter adjustment in green cognitive radio

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Journal of Systems Engineering and Electronics 2015年第5期26卷 897-907页

作者： Hongyuan Gao Chenwan Li School of Information and Communication Engineering Harbin Engineering University

Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm(QBFA)is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this *** superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service(Qo S)requirements.

关键词： green cognitive radio parameter adjustment quantum computing bacterial foraging algorithm

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Construction of supply chain coordination and optimization model of fresh food e-commerce platform based on improved bacterial foraging algorithm

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RAIRO-OPERATIONS RESEARCH 2022年第6期56卷 3853-3869页

作者： He, Juan Xian Univ Finance & Econ Sch Informat Xian 710100 Shaanxi Peoples R China

In order to maximize the overall profit of the supply chain of fresh food e-commerce platform, the supply chain coordination and optimization model of fresh food e-commerce platform based on the improved bacterial foraging algorithm is constructed. The basic model of bacterial foraging algorithm is constructed through chemotaxis, reproduction, elimination & dispersal, and the bacterial foraging algorithm is improved by using four parts: bacterial individual and parameter initialization, chemotaxis behavior, reproduction behavior and migration behavior, so as to realize the coordination and optimization of the supply chain of fresh food e-commerce platform. Use the Internet service platform to promote the electronization of the supply chain transaction process and improve the overall operation efficiency. Through the cooperation among fresh food suppliers, fresh food e-commerce and upstream fresh food suppliers, the supply chain coordination and optimization model of fresh food e-commerce platform is constructed to improve the overall profit of the supply chain. The experimental results show that using the improved bacterial foraging algorithm to solve the supply chain coordination and optimization model of fresh food e-commerce platform has high effectiveness, and can maximize the overall profit of the supply chain of fresh food e-commerce platform.

关键词： bacterial foraging algorithm fresh food e-commerce platform supply chain coordination

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Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2022年第1期41卷 340-358页

作者： Kasruddin Nasir, Ahmad Nor Ahmad, Mohd Ashraf Tokhi, M. Osman Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan Campus Pekan 26600 Pahang Malaysia London South Bank Univ Sch Engn Dept Elect & Elect Engn London England

A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well.

关键词： bacterial foraging algorithm flexible manipulator system PD-like fuzzy logic control spiral dynamic

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Solving fuzzy scheduling using clustering method and bacterial foraging algorithm

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SOFT COMPUTING 2023年第11期27卷 7285-7297页

作者： Li, Yingli Wang, Jiahai Gao, Jianxiang Liu, Zhengwei Tongji Univ Sch Mech Engn 4800 Caoan Rd Shanghai 201804 Peoples R China

Multi-objective fuzzy flexible job-shop scheduling problem (MFFJSSP) is a combination of multi-objective fuzzy scheduling and flexible job shop scheduling, which has higher complexity and is an NP-hard problem. For incompletely automated job shop, MFFJSSP is more important and conforms to the actual production. To solve the MFFJSSP, an effective multi-objective fuzzy scheduling method was proposed. First, a fuzzy number processing method based on fuzzy clustering that is more in line with the actual production was designed to replace the classic triangular fuzzy number. Second, based on the critical path theory, a more efficient method of critical operation block selection and critical operation movement were designed and applied them to the chemotaxis stage of bacterial foraging optimization algorithm to improve the accuracy of optimization and reduce invalid movement. Third, a novel decision tree-based bacterial reproduction method was designed to prevent the algorithm from falling into a local optimal solution. Finally, some cases were generated to verify the feasibility and effectiveness of the proposed algorithm from multiple dimensions.

关键词： Flexible job shop Multi-objective optimization bacterial foraging algorithm Fuzzy scheduling

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Improving bacterial foraging algorithm using non-uniform elimination-dispersal probability distribution

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ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 3341-3349页

作者： Sahib, Mouayad A. Abdulnabi, Ahmed R. Mohammed, Marwan A. Univ Informat Technol & Commun Coll Engn Baghdad Iraq Univ Informat Technol & Commun Business Informat Coll Baghdad Iraq Salahaddin Univ Software & Informat Engn Erbil Kurdistan Iraq

bacterial foraging optimization (BFO) is a swarm intelligence algorithm inspired from forging behavior of the E. coli bacteria. The BFO is based on three basic processes;chemotaxis, reproduction, and elimination-dispersal. In the elimination-dispersal process, a constant probability of elimination is assigned to all bacteria. The assignment is independent of bacteria's ranking in the population. Therefore, a bacterium which can be near to an optimal position may be replaced with one which is far away from the optimum solution, thus, affecting the convergence speed. In this paper an improved BFO algorithm (IBFOA) is proposed. The improvement lies in the elimination and dispersal event in which a non-uniform probability distribution is used. The non-uniform distribution is implemented by a linear and nonlinear probability distribution that replaces the conventional constant distribution. Simulation results show the efficiency of the proposed algorithm compared to the conventional bacterial foraging algorithm in terms of convergence speed. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.

关键词： bacterial foraging algorithm Metaheuristic algorithms Elimination-dispersal event Non-uniform probability distribution

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Knowledge worker scheduling optimization model based on bacterial foraging algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 124卷 330-337页

作者： Dan, Yufang Tao, Jianwen Ningbo Polytech Inst Artificial Intelligence Applicat Ningbo 315800 Peoples R China

bacterial foraging algorithm comes from the best survival selection mechanism of animals in nature. As the representative of the heuristic algorithm, the bacterial foraging algorithm has unique advantages in solving the multi difficulty scheduling problem effectively. In order to realize the artificial intelligent management of the enterprise's staff scheduling, this paper constructs the knowledge staff scheduling model by using a bacterial foraging algorithm and analyzes the implementation principle, advantages, and disadvantages of the algorithm. The influence of the basic parameters in the algorithm model on the algorithm performance is analyzed. In order to optimize the unconventional foraging strategy, the improvement measures of bacterial foraging behavior were proposed. Finally, the performance of the optimized bacterial foraging algorithm is tested and compared with the basic bacterial foraging algorithm, genetic algorithm, and particle swarm optimization algorithm. The experimental results show that the optimized bacterial foraging algorithm can achieve better convergence accuracy and shorter convergence speed for the objective function, and it can solve the scheduling optimization problem of knowledge workers more quickly, accurately, and effectively. The research in this paper shows that the optimization of four aspects of the basic bacterial foraging algorithm improves the performance of the algorithm and provides a theoretical reference for the optimization of the bacterial foraging algorithm. (C) 2021 Elsevier B.V. All rights reserved.

关键词： bacterial foraging algorithm Multi difficulty scheduling problem Artificial intelligence management Convergence accuracy Convergence rate Enterprise employee scheduling

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Harmonic Elimination and Optimization of Stepped Voltage of Multilevel Inverter by bacterial foraging algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2010年第4期5卷 545-551页

作者： Reza-Salehi Behrooz-Vahidi Naeem-Farokhnia Mehrdad-Abedi Amikabir Univ Technol Dept Elect Engn Tehran Iran

A new family of DC to AC converters, referred to as multilevel inverter, has received much attention from industries and researchers for its high power and voltage applications. One of the conventional techniques for implementing the switching algorithm in these inverters is optimized harmonic stepped waveform (OHSW). However, the major problem in using this technique is eliminating low order harmonics by solving the nonlinear and complex equations. In this paper, a new approach called the "bacterial foraging algorithm" (BFA) is employed. This algorithm eliminates and optimizes the harmonics in a multilevel inverter. This method has higher speed, precision, and convergence power compared with the genetic algorithm (GA), a famous evolutionary algorithm. The proposed technique can be expanded in any number of levels. The purpose of optimization is to remove some low order harmonics, as well as to ensure the fundamental harmonic retained at the desired value. As a case study, a 13-level inverter is chosen. The comparison results by MATLAB software between the two optimization methods (BFA and GA) have shown the effectiveness and superiority of BFA over GA where convergence is desired to achieve global optimum.

关键词： Multilevel Inverter Harmonic Elimination bacterial foraging algorithm Genetic algorithm

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Optimized Relative Transformation Matrix Using bacterial foraging algorithm for Process Fault Detection

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2016年第4期63卷 2595-2605页

作者： Yi, Jun Huang, Di Fu, Siyao He, Haibo Li, Taifu Chongqing Univ Sci & Technol Coll Elect & Informat Engn Chongqing 401331 Peoples R China Univ Massachusetts Dept Comp Sci Boston MA 02125 USA Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

Fault diagnosis of an aluminum electrolysis cell has long been a challenging industrial issue due to its inherent difficulty in extracting meaningful features from numerous nonlinear and highly coupled parameters. To solve this problem, this paper presents optimized relative transformation matrix (RTM) using bacterial foraging algorithm (BFA-ORTM). In particular, the operator of relative transformation is introduced to change the original variables in the spatial distribution and eigenvalues of the covariance matrix in the feature space. Then, optimization objective function on the comprehensive index., the squared prediction error (SPE), and Hotelling's T-squared (T 2) statistics are established. Furthermore, bacterial foraging algorithm is applied to obtain the optimized operator to facilitate extracting the representative principal components. Compared with traditional approaches, BFA-ORTM not only overcomes the drawback of losing feature after the normalization of nonlinear variables, but also improves the accuracy of fault diagnosis. Extensive experimental results on real-world aluminum electrolytic production process validated our proposed method's effectiveness.

关键词： Aluminum electrolytic production bacterial foraging algorithm fault detection kernel principal component analysis relative transformation

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