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An adaptive rejuvenation of bacterial foraging algorithm for global optimization

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期82卷 1965-1993页

作者： Khosla, Tejna Verma, Om Prakash Delhi Technol Univ Dept Comp Sci & Engn Delhi India Delhi Technol Univ Dept Elect & Commun Engn Delhi India

bacterial foraging algorithm (BFA) is a novel nature-inspired algorithm that mimics the social foraging behavior of E. coli. Bacteria. However, it gets stuck in the local optima trap and yields poor convergence in complex landscapes. To improve the exploration-exploitation balance and achieve the global optima quickly, this paper proposes a novel hybrid called the bacterial foraging algorithm-firefly algorithm (BFA-FA). In this work, two strategies namely adaptive strategy and leadership strategy are applied on conventional BFA. The performance is examined on standard, non-linear and CEC_2017 benchmark functions over several evaluation parameters. The results on benchmark functions show that BFA-FA provides accurate solutions, avoids local optima, works well on multimodal and multidimensional landscapes, and converges faster. It also shows the statistically significant difference among other algorithms. The proposed algorithm is applied on two classical engineering problems to validate its robustness and applicability.

关键词： Adaptation bacterial foraging algorithm Firefly algorithm Hybridization Optimization

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Economic emission load dispatch through fuzzy based bacterial foraging algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2010年第7期32卷 794-803页

作者： Hota, P. K. Barisal, A. K. Chakrabarti, R. UCE Dept Elect Engn Burla Orissa India Jadavpur Univ Dept Elect Engn Kolkata India

This paper presents a newly developed optimization approach involving a modified bacterial foraging algorithm (MBFA) applied for the solution of the economic and emission load dispatch (EELD) problem. The approach utilizes the natural selection of global optimum bacterium having successful foraging strategies in the fitness function. The bacterial foraging algorithm (BFA) appears to be a robust and reliable optimization algorithm for the solution of the EELD problems. To obtain the best compromising solution a fuzzy decision making approach using MBFA is applied to the standard IEEE 30-bus six generator test system and a Taiwan power system of 40 generating units with valve point loading effects. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, linear programming (LP), multi-objective stochastic search technique (MOSST), differential evolution (DE), non-dominated sorting genetic algorithm (NSGA), niched pareto genetic algorithm (NPGA), strength pareto evolutionary algorithm (SPEA) and fuzzy clustering based particle swarm optimization (FCPSO) performed in different central load dispatch centers to solve EELD problems. The quality and usefulness of the proposed algorithm is demonstrated through its application to two standard test systems in comparison with the other existing techniques. The current proposal was found to be better than, or at least comparable to them considering the quality of the solutions obtained. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Economic emission dispatch bacterial foraging algorithm Fuzzy decision making Multi-objective optimization

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Control of voltage profile with optimal control and placement of distributed generation using the refined bacterial foraging algorithm

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JOURNAL OF VIBRATION AND CONTROL 2014年第13期20卷 2006-2018页

作者： Jaganathan, S. Palaniswami, S. Anna Univ Madras 600025 Tamil Nadu India Govt Coll Engn Sulur Coimbatore 641402 Tamil Nadu India

This paper proposes a refined bacterial foraging algorithm (RBFA) for solving the multi-objective based optimal power dispatch with optimal placement of distributed generation (DG) to minimize the total real power loss, generation cost, the environmental emission and considering various controls and limits. The RBFA is based on the social foraging behavior of the Escherichia coli bacteria and its improved version of the basic bacterial foraging algorithm. The RBFA provides natural selection to eliminate poor foraging strategies for bacteria and to propagate other successful foraging strategies where foraging is proceeded using a position updating process, step length, search dimension and search direction with adaptation of basic foraging principles. Initially, the algorithm randomly generates the particle positions representing the size and location of DG and its proposal to solve the simultaneous optimization of the multi-objective problem. The proposed RBFA is used to determine the optimal sizes and locations of multi-DGs;the different types of DG are considered and the load flow is used to calculate the exact loss and to minimize simultaneously the economic cost and the emission of thermal units by changing the location and varying the sizes of the DG units. The test results indicate that the RBFA method can obtain better results than similar social behavior algorithm method on the IEEE30-bus system. The results are compared with and without DG units. The proposed method found the optimal location and sizing of DG units with control of the voltage profile, control of the cost of generation and control and reduction of environmental pollution and transmission losses.

关键词： bacterial foraging algorithm control of voltage profile control of optimal power dispatch distributed generation load flow solution refined bacterial foraging algorithm stability indices

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An accurate and economical approach for induction motor field efficiency estimation using bacterial foraging algorithm

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MEASUREMENT 2011年第4期44卷 674-684页

作者： Sakthivel, V. P. Bhuvaneswari, R. Subramanian, S. Annamalai Univ Fac Engn & Technol Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

As the demand of electrical energy increases, it is vital to replace inefficient motors with new energy efficient ones. The first step towards achieving this goal is to estimate the existing motors efficiencies accurately to determine how much energy saving will be achieved by using energy efficient motors. This paper proposes an economical and accurate approach for motor field efficiency estimation using bacterial foraging algorithm. The approach relies on the measurement of the stator current, stator voltage, input power, stator resistance and rotor speed of the motor without conducting no-load and locked-rotor tests. The BF algorithm is used to determine the equivalent circuit parameters of the motor. The efficiency is then estimated using these parameters. The advantages of the proposed method over the existing methods are simple procedure, efficiency can be estimated accurately without conducting any invasive tests and inexpensive. The approach has been tested on a 5 HP motor and the results are compared with particle swarm optimization method, immune algorithm method, torque gauge method, equivalent circuit method, slip method, current method and segregated loss method. The results demonstrate that the proposed approach can accurately estimate the field efficiency of motor. Accordingly, it is suitable for conducting energy audits and management of the motor. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： In-service motor bacterial foraging algorithm Parameter estimation Efficiency estimation Energy audit

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Adaptive control of DC motor using bacterial foraging algorithm

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APPLIED SOFT COMPUTING 2011年第8期11卷 4913-4920页

作者： Bhushan, Bharat Singh, Madhusudan Delhi Technol Univ Dept Elect Engn Delhi India

This paper introduces a bacterial foraging algorithm (BFA) based high performance speed control system for a DC motor. The rotor speed of the DC motor is being made to follow an arbitrary selected trajectory. The unknown nonlinear dynamics of the motor and the load are captured by BFA. The trained BFA identifier is used with a desired reference model to achieve trajectory control of DC motor. In this paper bacterial foraging algorithm (BFA) has been implemented for identification and control of DC motor. Simulation study on proposed system has been carried out in MATLAB. System nonlinearities alpha and beta have been estimated using BFA and compared with actual plant nonlinearities of dynamical system. In tracking of motor speed using BFA based controller the performance of the motor have been observed and compared with reference one. Performance study of DC motor has been carried out through genetic algorithm (GA) also. A comparison of performance analysis using BFA controller and that of GA for trajectory tracking shows that BFA based adaptive controller works effectively for tracking the desired trajectory in DC motor with less computational time. (C) 2011 Elsevier B. V. All rights reserved.

关键词： bacterial foraging algorithm Identification Adaptive control Genetic algorithm DC motor

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A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2015年第10期28卷 1106-1121页

作者： Zhao, Fuqing Jiang, Xin Zhang, Chuck Wang, Junbiao Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Northwestern Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ Xian 710072 Peoples R China

In this article, a chemotaxis-enhanced bacterial foraging optimisation (CEBFO) is proposed to solve the job shop scheduling problem more effectively. The new approach, which is based on a new chemotaxis with the differential evolution (DE) operator added, aims at solving the tumble failure problem in the tumble step and accelerates the convergence speed of the original algorithm. The effectiveness of the new chemotaxis and the convergence are proved theoretically and tested in continuous problems. Furthermore, a local search operator was designed, which can improve the local search ability of novel algorithm greatly. Finally, the experiments were conducted on a set of 38 benchmark problems of job shop scheduling and the results demonstrated the outperformance of the proposed algorithm.

关键词： chemotaxis job shop scheduling hybrid optimisation difference mutation bacterial foraging algorithm

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An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第12期38卷 15489-15498页

作者： Sanyal, Nandita Chatterjee, Amitava Munshi, Sugata BP Poddar Inst Managemenr & Technol Dept Elect Engn Kolkata 700052 India Jadavpur Univ Dept Elect Engn Kolkata 700032 India

In this paper an Adaptive bacterial foraging is proposed for fuzzy entropy optimization when it is applied to the segmentation of gray images. The proposed algorithm represents the improved version of classical bacterial foraging algorithm which is a newly developed stochastic optimization tool. This optimization technique is applied for optimization of the fitness function which is fuzzy entropy. Classical bacterial foraging algorithm is improved by adaptively selecting the exploitation and exploration state in chemotaxis of ***. bacteria. The newly developed algorithm is applied on benchmark gray images and proved to be suitable for thresholding based image segmentation. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： bacterial foraging algorithm Chemotaxis Image segmentation Bi level thresholding Fuzzy entropy

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A harmonic suppression method of variable frequency drive based on bacterial foraging algorithm

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2016年第1期7卷 76-86页

作者： Xiao, Dongming Wang, Yong He, Kuanfang Li, Xuejun Hunan Univ Sci & Technol Minist Educ Engn Res Ctr Adv Min Equipment Xiangtan 411201 Peoples R China

A harmonic suppression method of variable frequency drive (VFD) based on bacterial foraging algorithm is developed in this study. The objective function to minimise total harmonic distortion (THD) with certain constraints is established. In order to solve a series of nonlinear transcendental equation, use bacterial foraging algorithm to get the optimal switching time in a certain frequency range and different load to determine the switching time of the inverter power devices. The simulation and experiment results show that the application of bacterial foraging algorithm can more effectively suppress the harmonic amplitude in theory, and the total harmonic distortion is smaller.

关键词： total harmonic distortion THD bacterial foraging algorithm BFA pulse width modulation harmonic optimisation

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bacterial foraging algorithm for a Neural Network Learning Improvement in an Automatic Generation Controller

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ENERGIES 2023年第6期16卷 2802页

作者： Al-Majidi, Sadeq D. Altai, Hisham Dawood Salman Lazim, Mohammed H. Al-Nussairi, Mohammed Kh. Abbod, Maysam F. Al-Raweshidy, Hamed S. Univ Misan Coll Engn Dept Elect Engn Amarah 62001 Iraq Brunel Univ London Coll Engn Dept Elect & Elect Engn London UB8 3PH England

The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers' load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation.

关键词： automatic generation controller neural network model bacterial foraging algorithm hybrid power system photovoltaic power generation

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Synergy of Adaptive bacterial foraging algorithm and Particle Swarm Optimization algorithm for Image Segmentation

Synergy of Adaptive Bacterial Foraging Algorithm and Particl...

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IEEE International Conference on Circuits, Power and Computing Technologies (ICCPCT)

作者： Sasithradevi, A. Singh, N. Nirmal VV Coll Engn Elect & Commun Engn Thisaiyanvilai India

ISBN: (纸本)9781479923977

Many practical applications such as medical image segmentation, object detection, recognition tasks and video surveillance have need for accurate image segmentation techniques. Hence image segmentation is an important technique for image processing which is regarded as first step for image analysis. In this paper an image segmentation technique based on bacterial foraging (BF) and Particle Swarm Optimization (PSO) algorithm is addressed. Initially adaptation is done on BF algorithm by computing the step length using the number of variables in the search space. Further, on exhaustive analysis of BF algorithm, it was revealed that the tumble behavior will lead to random delay in searching optimal solutions and premature convergence. This synergy algorithm makes use of PSO in providing social information and adaptive BF algorithm in finding new optimal threshold values using elimination and dispersal. The proposed method has been applied to few benchmark images with promising results.

关键词： Image segmentation bacterial foraging algorithm Particle Swarm Optimization Bi-level Thresholding

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