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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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A novel bacterial foraging optimization algorithm for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 83卷 1-17页

作者： Chen, Yu-Peng Li, Ying Wang, Gang Zheng, Yue-Feng Xu, Qian Fan, Jia-Hao Cui, Xue-Ting Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China

bacterial foraging optimization (BFO) algorithm is a new swarming intelligent method, which has a satisfactory performance in solving the continuous optimization problem based on the chemotaxis, swarming, reproduction and elimination-dispersal steps. However, BFO algorithm is rarely used to deal with feature selection problem. In this paper, we propose two novel BFO algorithms, which are named as adaptive chemotaxis bacterial foraging optimization algorithm (ACBFO) and improved swarming and elimination dispersal bacterial foraging optimization algorithm (ISEDBFO) respectively. Two improvements are presented in ACBFO. On the one hand, in order to solve the discrete problem, data structure of each bacterium is redefined to establish the mapping relationship between the bacterium and the feature subset. On the other hand, an adaptive method for evaluating the importance of features is designed. Therefore the primary features in feature subset are preserved. ISEDBFO is proposed based on ACBFO. ISEDBFO algorithm also includes two modifications. First, with the aim of describing the nature of cell to cell attraction-repulsion relationship more accurately, swarming representation is improved by means of introducing the hyperbolic tangent function. Second, in order to retain the primary features of eliminated bacteria, roulette technique is applied to the elimination-dispersal phase. In this study, ACBFO and ISEDBFO are tested with 10 public data sets of UCI. The performance of the proposed methods is compared with particle swarm optimization based, genetic algorithm based, simulated annealing based, ant lion optimization based, binary bat algorithm based and cuckoo search based approaches. The experimental results demonstrate that the average classification accuracy of the proposed algorithms is nearly 3 percentage points higher than other tested methods. Furthermore, the improved algorithms reduce the length of the feature subset by almost 3 in comparison to other methods

关键词： bacterial foraging optimization algorithm Feature selection Classification Support vector machine

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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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bacterial foraging optimization algorithm for CH selection and Routing in Wireless Sensor Networks 3

Bacterial Foraging Optimization Algorithm for CH selection a...

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3rd IEEE International Conference on Recent Advances in Information Technology (RAIT)

作者： Lalwani, Praveen Das, Sagnik Indian Sch Mines Comp Sc & Engg Dhanbad Bihar India

ISBN: (纸本)9781479985791

bacterial foraging optimization algorithm (BFOA) is a widely accepted nature inspired global optimization algorithm. CH selection and Routing are well known techniques for enhancing the life of the wireless sensor networks (WSN). In two tired routing architecture, CH demises earlier due to its extra function. Therefore, proper care taken while selection of CH's. The current study focuses on solving both of the above mentioned problems using bacteria foraging algorithm. The CH selection algorithm is devised with new fitness function based on residual energy and distance. And the routing also proposed with novel fitness which considers energy and distance. The proposed algorithms are rigorously tested on different scenarios to show its performance and compared with conventional methods such as, EADC, DHCR and Hybrid Routing. Experimental results depicts that proposed algorithms performs better than existing ones.

关键词： bacterial foraging optimization algorithm WSN Cluster Head selection Routing

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Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm

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AIN SHAMS ENGINEERING JOURNAL 2016年第3期7卷 959-971页

作者： Devabalaji, K. R. Ravi, K. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India

In this paper, a novel approach to determine the optimal location and sizing of Distribution Generation (DG) and Distribution STATic COMpensator (DSTATCOM) is analyzed, and the objective function is formulated for minimizing power loss, operational costs and voltage profile enhancement of the system subjected to equality and in equality constraints. Loss sensitivity factor (LSF) is used to pre-determine the optimal location of DG and DSTATCOM. The bacterial foraging optimization algorithm (BFOA) is proposed to determine the optimal size of the DG and DSTATCOM. In this paper, the DG and DSTATCOM are simultaneously allocated in radial distribution system and it is analyzed with different load models. To check the feasibility, the proposed method is tested on IEEE 33-bus and 119-bus radial distribution system and the results were compared with other existing technique. (C) 2015 Ain Shams University. Production and hosting by Elsevier B.V.

关键词： bacterial foraging optimization algorithm Loss sensitivity factor Power loss minimization Voltage profile index Distribution system

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Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries

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JOURNAL OF APPLIED ELECTROCHEMISTRY 2016年第11期46卷 1119-1131页

作者： Ma, Yan Ru, Jingpei Yin, Mingyue Chen, Hong Zheng, Weitao Jilin Univ State Key Lab Automot Simulat & Control Changchun Peoples R China Jilin Univ Dept Control Sci & Engn Renmin St 5988 Changchun 130025 Peoples R China Jilin Univ Coll Mat Sci & Engn Renmin St 5988 Changchun 130025 Peoples R China

Batteries are key components in electric vehicles and energy storage systems. To estimate a battery's state of charge, monitor its state of health, and formulate a balanced strategy, a battery model that requires a shorter and less costly study period than a real battery is established. This paper aims to describe a single-particle model of a lithium-ion battery that has a simple structure, can be embedded in simulation software for online applications, and provides a high-accuracy characterization of the dynamics. The single-particle model, which is described by a set of partial differential equations, is a simplified electrochemical model that characterizes the dynamic voltage response. A procedure for reducing the model based on the three-parameter polynomial approximation and the volume-average integration method is proposed to simplify the partial differential equations of the single-particle model. Identifying the parameters in the battery model is the key problem. The convergent bacterial foraging optimization algorithm with a short computation time is adopted for identifying the electrochemical parameters, including the active surface areas of the electrodes, the diffusion coefficients of the lithium ions in the solid phase, and the reaction rate constants. Then, the single-particle model of a lithium-ion battery is set up in MATLAB and Simulink. Finally, the precision of the single-particle model is verified by comparing the terminal voltages of the battery and the model. The results show that the single-particle model of a lithium-ion battery is very accurate and simple, thus verifying the reliability of the parameter identification process.

关键词： Lithium-ion battery Single-particle model Three-parameter polynomial approximation Parameter identification bacterial foraging optimization algorithm

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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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Optimal location and sizing of capacitor placement in radial distribution system using bacterial foraging optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 71卷 383-390页

作者： Devabalaji, K. R. Ravi, K. Kothari, D. P. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India Gaikwad Patil Grp Inst Nagpur Nagpur Maharashtra India

This study introduces a new long term scheduling for optimal allocation of capacitor bank in radial distribution system with the objective of minimizing power loss of the system subjected to equality and in equality constraints. In the proposed method the new integrated approach of Loss Sensitivity Factor (LSF) and Voltage Stability Index (VSI) are implemented to determine the optimal location for installation of capacitor banks. bacterial foraging optimization algorithm (BFOA) is proposed to find the optimal size of the capacitor banks. The proposed method is applied on IEEE 34-bus and 85-bus radial distribution system with all possible load changes. The load is varied from light load (50%) to peak load (160%) with a step size of 1% and optimization procedure is followed to entire period. The generalized equation obtained from the curve fitting technique is very much helpful for the Distribution Network Operators (DNOs) to adjust the capacitor size according to the load changes. The simulated results demonstrate well the performance and effectiveness of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： bacterial foraging optimization algorithm Capacitor placement Power loss minimization Loss sensitivity factor Voltage stability index

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bacterial foraging optimization algorithm for assembly line balancing

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NEURAL COMPUTING & APPLICATIONS 2014年第1期25卷 237-250页

作者： Atasagun, Yakup Kara, Yakup Selcuk Univ Dept Ind Engn Konya Turkey

Assembly line balancing is the problem of assigning tasks to workstations by optimizing a performance measure while satisfying precedence relations between tasks and cycle time restrictions. Many exact, heuristic and metaheuristic approaches have been proposed for solving simple straight and U-shaped assembly line balancing problems. In this study, a relatively new optimization algorithm, bacterial foraging optimization algorithm (BFOA), based heuristic approach is proposed for solving simple straight and U-shaped assembly line balancing problems. The performance of the proposed algorithm is evaluated using a well-known data set taken from the literature in which the number of tasks varies between 7 and 111, and results are also compared with both an ant-colony-optimization-based heuristic approach and a genetic-algorithm-based heuristic approach. The proposed algorithm provided optimal solutions for 123 out of 128 (96.1 %) test problems in seconds and is proven to be promising.

关键词： Assembly line balancing U-shaped assembly lines bacterial foraging optimization algorithm Metaheuristics

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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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