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Статті в журналах з теми "BFOA"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Lenin, K. "ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 9 (September 30, 2018): 212–19. http://dx.doi.org/10.29121/granthaalayah.v6.i9.2018.1222.

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Анотація:

This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simulation results demonstrate good performance of the ABFPSO in solving an optimal reactive power problem. In order to evaluate the proposed algorithm, it has been tested on IEEE 57 bus system and compared to other algorithms.

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2

Wang, Xingzhong, Xinghua Kou, Jinfeng Huang, and Xianchun Tan. "A Collision Avoidance Method for Intelligent Ship Based on the Improved Bacterial Foraging Optimization Algorithm." Journal of Robotics 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/6661986.

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Анотація:

The bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly, optimizes inaccurately, and has low stability. To fix the above shortcomings of BFOA, an autonomous collision avoidance algorithm based on the improved bacterial foraging optimization algorithm (IBFOA) is demonstrated in this paper. An adaptive diminishing fractal dimension chemotactic step length is designed to replace the fixed step length to achieve the adaptive step length adjustment, an optimal swimming search method is proposed to solve the invalid searching and repeated searching problems of the traditional BFOA, and the adaptive migration probability is developed to take the place of the fixed migration probability to prevent elite individuals from being lost in BOFA. The simulation of benchmark tests shows that the IBFOA has a better convergence speed, optimized accuracy, and higher stability; according to a collision avoidance simulation of intelligent ships which applies the IBFOA, it can realize the autonomous collision avoidance of intelligent ships in dynamic obstacles environment is quick and safe. This research can also be used for intelligent collision avoidance of automatic driving ships.

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3

Zambrano Zambrano, Dannyll Michellc, Darío Vélez, Yohanna Daza, and José Manuel Palomares. "Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function." Enfoque UTE 10, no. 3 (September 30, 2019): 67–80. http://dx.doi.org/10.29019/enfoque.v10n3.490.

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Анотація:

This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions.

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4

Hernández-Ocaña, Betania, José Hernández-Torruco, Oscar Chávez-Bosquez, Maria Calva-Yáñez, and Edgar Portilla-Flores. "Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid." Applied Sciences 9, no. 6 (March 26, 2019): 1261. http://dx.doi.org/10.3390/app9061261.

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An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.

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5

Teja, Venigalla Sai, Chilakapati Srinivas, and P. Radhika. "Plant Disease Detection and Classification Using Bacteria Foraging Optimization Algorithm Through Convolution Neural Network." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3567–76. http://dx.doi.org/10.1166/jctn.2020.9233.

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Анотація:

Humans can recognize the plants infected by diseases but separated from our visual perception it is hard to recognize plant diseases. In croplands without taking the right care and prompt action, the entire field may become a region afflicted by diseases. So we identify the plant diseases ahead of time with the assistance of present-day computer technologies. An advanced model was introduced to accurately recognize and classification plant diseases. Here we proposed an approach that can use the Convolutional Neural Network (CNN) based on BFOA for distinguishing diseases in plants. The input picture for the extraction of features is divided into 3 clusters by the Euclidean distance measurement metric of the k-mean algorithm and from the ROI, parameters of the GLCM matrix are calculated in the same cluster prior to BFOA. Assigning matrix parameters as BFOA input improves the network’s accuracy and efficiency in determining. In classification, we proposed a Convolutional Neural Network (CNN) using ResNet50 as a pre-trained network in deep learning toolbox which classifies from a given dataset. The approach is more reliable as the detection and classification of plant diseases are more precise.

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6

Sodsri, Parichart, Bongkoj Sookananta, and Mongkol Pusayatanont. "Optimal Placement of Distributed Generation Using Bacterial Foraging Optimization Algorithm." Applied Mechanics and Materials 781 (August 2015): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amm.781.329.

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This paper presents the determination of the optimal distributed generation (DG) placement using bacterial foraging optimization algorithm (BFOA). The BFO mimics the seeking-nutrient behavior of the E. coli bacteria. It is utilized here to find the location and size of the DG installation in radial distribution system in order to obtain minimum system losses. The operation constraints include bus voltage limits, distribution line thermal limits, system power balance and generation power limits. The algorithm is tested on the IEEE 33 bus system. The result shows that the algorithm could be used as an alternative to the other techniques and improvement of the algorithm is required for acceleration and better accuracy of the calculation.

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7

Abdul Razak, Intan Azmira Wan, Izham Zainal Abidin, Yap Keem Siah, Aidil Azwin Zainul Abidin, Titik Khawa Abdul Rahman, Nurliyana Baharin, and Mohd Hafiz Bin Jali. "An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 748. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp748-755.

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Анотація:

<span lang="EN-US">Predicting electricity price has now become an important task in power system operation and planning. An hour-ahead forecast provides market participants with the pre-dispatch prices for the next hour. It is beneficial for an active bidding strategy where amount of bids can be reviewed or modified before delivery hours. However, only a few studies have been conducted in the field of hour-ahead forecasting. This is due to most power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than single-settlement system (real time). Therefore, a hybrid multi-optimization of Least Square Support Vector Machine (LSSVM) and Bacterial Foraging Optimization Algorithm (BFOA) was designed in this study to produce accurate electricity price forecasts with optimized LSSVM parameters and input features. So far, no works has been established on multistage feature and parameter optimization using LSSVM-BFOA for hour-ahead price forecast. The model was examined on the Ontario power market. A huge number of features were selected by five stages of optimization to avoid from missing any important features. The developed LSSVM-BFOA shows higher forecast accuracy with lower complexity than most of the existing models.</span>

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8

Zheng, S., A. N. Jiang, X. R. Yang, and G. C. Luo. "A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm." Advances in Civil Engineering 2020 (August 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/3897215.

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Classification of the surrounding rock is the basis of tunnel design and construction. However, conventional classification methods do not allow dynamic tunnel construction adjustments because they are time-consuming and do not consider the randomness of rock mass. This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). The LSSVM is adopted to express the implicit relationship between classification indicators and rock mass grades, which is a response surface function for reliability evaluation. LSSVM parameters were optimized by the BFOA to form a hybrid BFOA-LSSVM algorithm. Using geological prediction and rock strength resilience results as classification indicators, samples were developed to train the LSSVM model using the hybrid algorithm. The Monte Carlo sampling method of reliability classification was implemented and applied to the Suqiao tunnel at the Puyan highway in the Fujian province of China; the influence of parameters on the performance of the algorithm is discussed. The results indicate that the new method is feasible for tunnel engineering; it can improve the classification accuracy of surrounding rock exhibiting randomness, to provide an effective means of classifying surrounding rock in the dynamic design of tunnel construction.

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9

Elsisi, M., M. Soliman, M. A. S. Aboelela, and W. Mansour. "ABC Based Design of PID Controller for Two Area Load Frequency Control with Nonlinearities." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 1 (October 1, 2015): 58. http://dx.doi.org/10.11591/tijee.v16i1.1588.

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This paper presents an application of the Artificial Bee Colony (ABC) to optimize the parameters of Proportional-Integral-Derivative controller (PID) of nonlinear Load Frequency Controller (LFC) for a power system. A two area non reheat thermal system is equipped with PID controller. ABC is employed to search for optimal controller parameters to minimize the time domain objective function. The performance of the proposed technique has been evaluated with the performance of the conventional Ziegler Nichols (ZN) , Genetic Algorithm (GA) and Bacterial Foraging Optimization Algorithm (BFOA) in order to demonstrate the superior efficiency of the proposed ABC in tuning PID controller. By comparison with the conventional technique, GA and BFOA, the effectiveness of the proposed ABC is validated over different operating conditions, and system parameters variations.

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10

Zhu, Yuanyuan, Shijie Su, Yuchen Qian, Yun Chen, and Wenxian Tang. "Parameter Optimization for Ship Antiroll Gyros." Applied Sciences 10, no. 2 (January 16, 2020): 661. http://dx.doi.org/10.3390/app10020661.

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Ship antiroll gyros are a type of equipment used to reduce ships’ roll angle, and their parameters are related to the parameters of a ship and wave, which affect gyro performance. As an alternative framework, we designed a calculation method for roll reduction rate and considered random waves to establish a gyro parameter optimization model, and we then solved it through the bacteria foraging optimization algorithm (BFOA) and pattern search optimization algorithm (PSOA) to obtain optimal parameter values. Results revealed that the two methods could effectively reduce the overall mass and floor space of the antiroll gyro and improved its antirolling effect. In addition, the convergence speed and antirolling effect of the BFOA were better than that of the PSOA.

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11

Yongqiang, Bao, Xi Ji, and Xu Haiyan. "Practical Speech Emotion Recognition Based on Im-BFOA." Journal of Applied Sciences 13, no. 22 (November 1, 2013): 5349–55. http://dx.doi.org/10.3923/jas.2013.5349.5355.

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12

Benmachiche, A., A. Makhlouf, and T. Bouhadada. "Optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (September 28, 2020): 171–81. http://dx.doi.org/10.3233/kes-200039.

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Анотація:

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.

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13

Srinivasulu, G., B. Subramanyam, and Surya Kalavathi M. "Transmission Expansion Planning Using Bacterial Foraging Optimization Algorithm (BFOA)." i-manager's Journal on Power Systems Engineering 2, no. 2 (July 15, 2014): 9–15. http://dx.doi.org/10.26634/jps.2.2.2928.

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14

Sri Krishna Sarath, P., and I. Swetha Monica. "Application of BFOA in Two Area Load Frequency Control." International Journal of Engineering & Technology 7, no. 3.31 (August 24, 2018): 50. http://dx.doi.org/10.14419/ijet.v7i3.31.18200.

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Анотація:

This paper presents Bacterial foraging optimization algorithm which is based on food searching process of Escherichia coli bacteria, which is gaining popularity due to its effectiveness and providing solution to real world optimization problems .BFOA is applied to control the parameter optimization of load frequency controller for tuning the parameters of the proportional integral and derivative controller. A simple two area system with thermal-thermal generating units is considered for simulation study which is controlled with PID controller. The main objective of this work is to design the controller by minimizing objective function .simulation studies demonstrate that our proposed controller is effective and transients are suppressed predominantly

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15

Ju, Hae-ji, and Soo-kyung Jeon. "Effect of Ultrasound Irradiation on the Properties and Sulfur Contents of Blended Very Low-Sulfur Fuel Oil (VLSFO)." Journal of Marine Science and Engineering 10, no. 7 (July 17, 2022): 980. http://dx.doi.org/10.3390/jmse10070980.

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Quality issues concerning very low-sulfur fuel oil (VLSFO) have increased significantly since the IMO sulfur-limit regulation became mandatory in 2020, as most VLSFO is produced by blending high-sulfur fuel oil (HSFO) with VLSFO. For instance, the conversion of VLSFO paraffins (C19 or higher alkanes) into waxes at low temperatures adversely affects cold flow properties. This study investigates the effects of ultrasonication on the chemical composition, dispersion stability, and sulfur content of samples prepared by blending ISO-F-DMA-grade marine gas oil (i.e., VLSFO) and ISO-F-RMG-grade marine heavy oil (i.e., HSFO) in volumetric ratios of 25:75 (BFO1), 50:50 (BFO2), and 75:25 (BFO3). The paraffin content decreased by 19.2% after 120 min of ultrasonic irradiation for BFO1 by 16.8% after 30 min for BFO3. The decrease in the content of high-molecular-weight compounds was faster at higher HSFO content; however, ultrasonication for longer-than-optimal times induced reaggregation, and thus, increased the content of high-molecular-weight compounds and decreased dispersion stability. In addition, ultrasonication did not significantly affect the sulfur content of BFO1 but decreased those of BFO2 (by 19% after 60 min) and BFO3 (by 25% after 30 min). Desulfurization efficiency increased with the increasing content of HSFO, as water present therein acted as an oxidant for oxidative desulfurization.

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16

Barisal, Ajit Kumar, and Deepak Kumar Lal. "Application of Moth Flame Optimization Algorithm for AGC of Multi-Area Interconnected Power Systems." International Journal of Energy Optimization and Engineering 7, no. 1 (January 2018): 22–49. http://dx.doi.org/10.4018/ijeoe.2018010102.

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Анотація:

A novel attempt has been made to use Moth Flame Optimization (MFO) algorithm to optimize PI/PID controller parameters for AGC of power system. Four different power systems are considered in the present article. Initially, a two area thermal power system is considered for simulation. The superiority of the proposed MFO optimized PI/PID controller has been demonstrated by comparing the results with recently published approaches such as conventional, GA, BFOA, DE, PSO, Hybrid BFOA-PSO, FA and GWO algorithm optimized PI/PID controller for the same power system model. Then, a sensitivity analysis is carried out to study the robustness of the system to wide changes in the operating conditions and system parameters from their nominal values. The proposed approach is extended to different realistic multi-area multi-source power systems with diverse sources of power generations for simulation study. The acceptability and efficacy of the proposed technique is demonstrated by comparing with other recently published techniques.

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17

Sahoo, Dillip Kumar, Rabindra Kumar Sahu, and Sidharth Panda. "Fractional Order Fuzzy PID Controller for Automatic Generation Control of Power Systems." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 1 (March 2, 2021): 71. http://dx.doi.org/10.37936/ecti-eec.2021191.222284.

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Анотація:

In this study, a Hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) tuned Fractional Order Fuzzy PID (FOFPID) structure is suggested for AGC of power systems. At first, a non-reheat type two-area thermal system is considered and the improvement of the proposed approach over Bacteria Foraging Optimization Algorithm (BFOA), Teaching Learning Based Optimization (TLBO), Jaya Algorithm (JA), Genetic Algorithm (GA) and Hybrid BFOA and Particle Swarm Optimization Algorithm (hBFOA-PSO) for the identical test systems has been demonstrated. The analysis was then extended to interconnected thermal power system of reheat type and two-area six-unit system. The results are compared with Firefly Algorithm (FA), Symbiotic Organism Search Algorithm (SOSA) and Artificial Bee colony (ABC) for second test system and TLBO, Hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS), ADE and hADE-PS tuned PID for third test system. Finally, robustness of the suggested controller is examined under varied conditions.

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18

Kumari, G. Vimala, G. Sasibhushana Rao, and B. Prabhakara Rao. "NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION." Image Analysis & Stereology 37, no. 3 (December 6, 2018): 249. http://dx.doi.org/10.5566/ias.1865.

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For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.

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19

Kaur, Mandeep, and Sanjay Kadam. "Bio-Inspired Workflow Scheduling on HPC Platforms." Tehnički glasnik 15, no. 1 (March 4, 2021): 60–68. http://dx.doi.org/10.31803/tg-20210204183323.

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Efficient scheduling of tasks in workflows of cloud or grid applications is a key to achieving better utilization of resources as well as timely completion of the user jobs. Many scientific applications comprise several tasks that are dependent in nature and are specified by workflow graphs. The aim of the cloud meta-scheduler is to schedule the user application tasks (and the applications) so as to optimize the resource utilization and to execute the user applications in minimum amount of time. During the past decade, there have been several attempts to use bio-inspired scheduling algorithms to obtain an optimal or near optimal schedule in order to minimize the overall schedule length and to optimize the use of resources. However, as the number of tasks increases, the solution space comprising different tasks-resource mapping sequences increases exponentially. Hence, there is a need to devise mechanisms to improvise the search strategies of the bio-inspired scheduling algorithms for better scheduling solutions in lesser number of iterations/time. The objective of the research work in this paper is to use bio-inspired bacteria foraging optimization algorithm (BFOA) along with other heuristics algorithms for better search of the scheduling solution space for multiple workflows. The idea is to first find a schedule by the heuristic algorithms such as MaxMin, MinMin, and Myopic, and use these as initial solutions (along with other randomly generated solutions) in the search space to get better solutions using BFOA. The performance of our approach with the existing approaches is compared for quality of the scheduling solutions. The results demonstrate that our hybrid approach (MinMin/Myopic with BFOA) outperforms other approaches.

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20

Hernández-Ocaña, Betania, Ma Del Pilar Pozos-Parra, Efrén Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, and Maria Bárbara Calva-Yáñez. "Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms." Computational Intelligence and Neuroscience 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/4525294.

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Анотація:

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

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21

Nayak, Smrutiranjan, Sanjeeb Kumar Kar, and Subhransu Sekhar Dash. "Optimized CFPID controller for SCiWOA in allocated grid." Journal of Statistics & Management Systems 26, no. 1 (2023): 241–47. http://dx.doi.org/10.47974/jsms-973.

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Анотація:

In persistently expanding region and construction of current power system having trouble request vulnerabilities, the utilization of proficient and lively recurrence power methodology is fundamental for palatable working of Power-System. In this examination, a CFPID regulator is recommended for regularity management of Power-System. To upgrade the regulator boundaries, a Sine Cosine enhanced Whale-Optimization-Algorithm is used. It’s first benefits of the SCiWOA changed CFPID regulator over hPSO-PS changed Fuzzy-PI regulator, hybrid-BFOA PSO changed PI regulator, GA changed PI regulator, BFOA changed PI regulator, Jaya Algorithm (JA) changed PIDN (PID with filter) regulator & TLBO changed PID regulator are exhibited for 2-region non warm nuclear energy network. The 2nd benefits of the Sine-Cosine-iWOA changed CFPID regulator on top of Artificial Bee Colony changed PID regulator, SOSA (Symbiotic Organism Search Algorithm) changed PID regulator and FA changed PID regulator are shown for two region warm nuclear energy network. Here SCiWOA based Combined-FPID regulator is too powerful in regulating the repeat respective with PID controller.

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22

Hassan, Elia Erwani, Titik Khawa Abdul Rahman, Zuhaina Zakaria, and Nazrulazhar Bahaman. "The Improved of BFOA for Ensuring the Sustainable Economic Dispatch." Applied Mechanics and Materials 785 (August 2015): 83–87. http://dx.doi.org/10.4028/www.scientific.net/amm.785.83.

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Анотація:

This paper introduced a new heuristic method the Improved to Bacterial Foraging Optimization Algorithm or IBFO to provide minimize objective functions in Secured Environmental Economic Dispatch (SEED) problems. An optimization problem may involve the highly non linear, non convex and non differentiable tends the solutions observed from a multiple local minima. The limitation faced by conventional methods are being trapped at any this local minima and prevent to reach the global minima. For that reason, this approach IBFO is tested under IEEE 118 bus system to obtain the minimum total cost function with less emission involved. Additionally, the proposed optimization approach is compared to original Bacterial Foraging Optimization Algorithm (BFO). As a result, all findings supported the novel IBFO as the competent and reliable technique.

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23

Kumar, Rajeev, Sourav Diwania, Pavan Khetrapal, Sheetal Singh, and Manoj Badoni. "Multimachine stability enhancement with hybrid PSO-BFOA based PV-STATCOM." Sustainable Computing: Informatics and Systems 32 (December 2021): 100615. http://dx.doi.org/10.1016/j.suscom.2021.100615.

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24

CH. VENKATESWARA, RAO, S. S. TULSIRAM, B. BRAHMAIAH, and CH RAMYA. "FEATURES OF PSO - BFOA BASED INCREMENT CONDUCTANCE METHOD WITH FPGA." i-manager's Journal on Circuits and Systems 7, no. 1 (2019): 14. http://dx.doi.org/10.26634/jcir.7.1.15391.

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25

Zhang, Jiangjiang, Zhihua Cui, Yechuang Wang, Hui Wang, Xingjuan Cai, Jinjun Chen, and Wuzhao Li. "A Coupling Approach With GSO-BFOA for Many-Objective Optimization." IEEE Access 7 (2019): 120248–61. http://dx.doi.org/10.1109/access.2019.2937538.

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26

Manoharan, Neelamegam, Subhransu Sekhar Dash, Kurup Sathy Rajesh, and Sidhartha Panda. "Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller." International Journal of Computers Communications & Control 12, no. 4 (June 29, 2017): 533. http://dx.doi.org/10.15837/ijccc.2017.4.2751.

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Анотація:

A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system.

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27

Motiram, Pawar Shalikram, and Subhash Shankar Zope. "Optimum Load Frequency Control in Power System using BFOA and CA." International Journal of Innovations in Engineering and Science 6, no. 11 (September 28, 2021): 8. http://dx.doi.org/10.46335/ijies.2021.6.11.3.

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28

Nair, S. Anu H., and P. Aruna. "Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems." Alexandria Engineering Journal 54, no. 4 (December 2015): 1161–74. http://dx.doi.org/10.1016/j.aej.2015.07.002.

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29

Kumar, Amandeep, Vishal Kumar Goar, Manoj Kuri, and Mohit Srivastava. "Directional antenna with reproduction optimisation (BFOA) used in mobile ad-hoc network." International Journal of Mobile Network Design and Innovation 10, no. 3 (2022): 141. http://dx.doi.org/10.1504/ijmndi.2022.10051468.

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30

Kuri, Manoj, Mohit Srivastava, Vishal Kumar Goar, and Amandeep Kumar. "Directional antenna with reproduction optimisation (BFOA) used in mobile ad-hoc network." International Journal of Mobile Network Design and Innovation 10, no. 3 (2022): 141. http://dx.doi.org/10.1504/ijmndi.2022.126449.

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31

Gupta, Prateek, and Ajay K. Sharma. "Designing of energy efficient stable clustering protocols based on BFOA for WSNs." Journal of Ambient Intelligence and Humanized Computing 10, no. 2 (February 26, 2018): 681–700. http://dx.doi.org/10.1007/s12652-018-0719-1.

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32

Awais, Muhammad, Nadeem Javaid, Khursheed Aurangzeb, Syed Haider, Zahoor Khan, and Danish Mahmood. "Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids." Energies 11, no. 11 (November 12, 2018): 3125. http://dx.doi.org/10.3390/en11113125.

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Nowadays, automated appliances are exponentially increasing. Therefore, there is a need for a scheme to accomplish the electricity demand of automated appliances. Recently, many Demand Side Management (DSM) schemes have been explored to alleviate Electricity Cost (EC) and Peak to Average Ratio (PAR). In this paper, energy consumption problem in a residential area is considered. To solve this problem, a heuristic based DSM technique is proposed to minimize EC and PAR with affordable user’s Waiting Time (WT). In heuristic techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Flower Pollination Algorithm (FPA) are implemented.

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33

Pappachen, Abhijith, and A. Peer Fathima. "BFOA Based FOPID Controller for Multi Area AGC System with Capacitive Energy Storage." International Journal on Electrical Engineering and Informatics 7, no. 3 (September 30, 2015): 429–42. http://dx.doi.org/10.15676/ijeei.2015.7.3.6.

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34

Nandakumar, E., R. Dhanasekaran, and N. K. Senapathi. "Real and Reactive Power Compensation Using UPFC by Bacterial Foraging Optimization Algorithm (BFOA)." Research Journal of Applied Sciences, Engineering and Technology 9, no. 11 (April 15, 2015): 1027–33. http://dx.doi.org/10.19026/rjaset.9.2596.

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35

Ali, E. S., and S. M. Abd-Elazim. "Optimal SSSC Design for Damping Power Systems Oscillations via Hybrid BFOA-PSO Approach." JES. Journal of Engineering Sciences 41, no. 3 (May 1, 2013): 1127–50. http://dx.doi.org/10.21608/jesaun.2013.114785.

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36

Roy, Kallol, Kamal Krishna Mandal, Atis Chandra Mandal, and Sankar Narayan Patra. "Analysis of energy management in micro grid – A hybrid BFOA and ANN approach." Renewable and Sustainable Energy Reviews 82 (February 2018): 4296–308. http://dx.doi.org/10.1016/j.rser.2017.07.037.

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37

Liang, Dong Ying, and Wei Kun Zheng. "An Intelligent Feature Selection Method Based on the Bacterial Foraging Algorithm." Applied Mechanics and Materials 50-51 (February 2011): 304–8. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.304.

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This paper puts forward an agent genetic algorithm based on bacteria foraging strategy (BFOA-L) as the feature selection method. The algorithm introduces the bacteria foraging (BF) behavior, and integrates the neural network and link agent structure to achieve fuzzy logic reasoning, so that the weights with no definite physical meaning in traditional neural network are endowed with the physical meaning of fuzzy logic reasoning parameters. The algorithm can maintain the diversity of the agent, so as to achieve satisfactory global optimization precision. The test result shows that this algorithm has good stability, little time complexity and high recognition accuracy.

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38

B. Venkata, Srikanth, and Lakshmi Devi Ai. "Saddle Node Bifurcation Point Analysis of Voltage Stability of IEEE 30-BUS Power System for Removal of Generation through Bacteria Foraging Optimization Algorithm." International Journal of Engineering & Technology 7, no. 3.31 (August 24, 2018): 36. http://dx.doi.org/10.14419/ijet.v7i3.31.18196.

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Анотація:

This paper deals with the identification of instability nodes of IEEE 30 BUS power system to generation removal. Optimal sizing and locations of reactive power compensations are obtained. Firstly one of the generators is assumed to be removed from service and the saddle node bifurcation (SNB) point voltages are evaluated without reactive power compensation. Secondly two generators are assumed to be removed from service and the saddle node point voltage magnitudes are obtained without reactive power compensation. For both cases the study is conducted by placing optimal reactive power compensations at optimal locations using Bacterial Foraging Optimization Algorithm (BFOA).

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39

Karuppiah, N., S. Muthubalaji, S. Ravivarman, Md Asif, and Abhishek Mandal. "Enhancing the performance of Transmission Lines by FACTS Devices using GSA and BFOA Algorithms." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 203. http://dx.doi.org/10.14419/ijet.v7i4.6.20463.

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Flexible Alternating Current Transmission System devices have numerous applications in electrical transmission lines like improvement of voltage stability, reactive power compensation, congestion management, Available Transfer Capacity enhancement, real power loss reduction, voltage profile improvement and much more. The effectiveness of these FACTS devices is enhanced by the placement of these devices in the transmission lines. The placement is based on transmission line sensitivity factors such as Bus voltage stability index and line voltage stability index. This research article focuses on optimizing the location, number and ratings of FACTS devices using Evolutionary Algorithms like Bacterial Foraging Algorithm and Gravitational search algorithm. FACTS devices such as Static Var Compensator, Thyristor Controlled Series Capacitor and Unified Power Flow Controller are placed on IEEE 14 bus and IEEE 30 bus systems for reducing the real power loss in the transmission system. The results show that the performance of the transmission lines is enhanced more using Bacterial Foraging Algorithm than Gravitational Search Algorithm.

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40

A.Minshed, Mohammed. "Bacterial Foraging Optimization Algorithm (BFOA) to Simulate Thermal Distribution in Yb:YAG Laser Thin Disk." Engineering and Technology Journal 31, Issue1A (January 1, 2013): 132–55. http://dx.doi.org/10.30684/etj.2013.71254.

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41

Ali, E. S., and S. M. Abd-Elazim. "BFOA based design of PID controller for two area Load Frequency Control with nonlinearities." International Journal of Electrical Power & Energy Systems 51 (October 2013): 224–31. http://dx.doi.org/10.1016/j.ijepes.2013.02.030.

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42

Saranya, Mary, Rajapandiyan A, Fathima K., Hema S, GeethaPriya S, and Saravanan S. "A Power System Stabilizer for Multi-Machine Power Based on Hybrid BF0A-PSO." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 2 (April 1, 2015): 213. http://dx.doi.org/10.11591/ijece.v5i2.pp213-220.

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Анотація:

<p>Bacterial Swarm Optimization (BSO) is used to design Power System Stabilizers in a multi machine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual’s best location and the global best location of PSO. The hybrid BFOA-PSO algorithm has been applied to IEEE 14 bus test system under normal, light and heavy load conditions. Simulations results have revealed the strength of the BSO in tuning Power System Stabilizers under normal, light and heavy load conditions. The results present the effectiveness of the controller to improve the power system stability over a different range of loading conditions.</p>

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43

Kamyab, Shima, and Abbas Bahrololoum. "Designing of rule base for a TSK- fuzzy system using bacterial foraging optimization algorithm (BFOA)." Procedia - Social and Behavioral Sciences 32 (2012): 176–83. http://dx.doi.org/10.1016/j.sbspro.2012.01.028.

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44

Panda, Sidhartha, Banaja Mohanty, and P. K. Hota. "Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems." Applied Soft Computing 13, no. 12 (December 2013): 4718–30. http://dx.doi.org/10.1016/j.asoc.2013.07.021.

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45

Van Hung, Phan, Dang Quang Viet, Nguyen Minh Duc, and Thanh Dat Le. "Ship routing optimization using bacterial foraging optimization algorithm for safety and efficient navigation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 2309. http://dx.doi.org/10.11591/ijece.v13i2.pp2309-2315.

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Efficient operation plays a vital role to develop a sustainable shipping fleet with cost competitive. The requirements for economic efficiency, energy efficiency, reducing emissions, and increasing safety and security lead to an innovative model in the optimal weather routing system. The vessel routing is influenced by the quality of meteorological and oceanographic data such as wind, waves, and currents. In this study, the model optimization of weather routing considers the meteorological and oceanographic information, ship's characteristics combined with an adaptive bacterial foraging optimization algorithm (BFOA) will be introduced and applied to the ship’ navigation at sea. The simulation results will be evaluated the effectiveness and reliability of the model. This model will support ships’ navigation to be safer and more comfortable, operate more efficiently and reduce emissions.

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46

Reddy, C. H. Venugopal, and P. Siddaiah. "Comparative Analysis of Dual Secure Based Medical Image Watermarking Technique to Increase Security of Watermark Data Using BFOA." International Journal of Computer and Communication Engineering 5, no. 6 (2016): 381–97. http://dx.doi.org/10.17706/ijcce.2016.5.6.381-397.

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47

Darwish, Hiba H., and Ayman Al-Quraan. "Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF." Sustainability 15, no. 4 (February 10, 2023): 3270. http://dx.doi.org/10.3390/su15043270.

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Estimating wind energy at a specific wind site depends on how well the real wind data in that area can be represented using an appropriate distribution function. In fact, wind sites differ in the extent to which their wind data can be represented from one region to another, despite the widespread use of the Weibull function in representing the wind speed in various wind locations in the world. In this study, a new probability distribution model (normal PDF) was tested to implement wind speed at several wind locations in Jordan. The results show high compatibility between this model and the wind resources in Jordan. Therefore, this model was used to estimate the values of the wind energy and the extracted energy of wind turbines compared to those obtained by the Weibull PDF. Several artificial intelligence techniques were used (GA, BFOA, SA, and a neuro-fuzzy method) to estimate and predict the parameters of both the normal and Weibull PDFs that were reflected in conjunction with the actual observed data of wind probabilities. Afterward, the goodness of fit was decided with the aid of two performance indicators (RMSE and MAE). Surprisingly, in this study, the normal probability distribution function (PDF) outstripped the Weibull PDF, and interestingly, BFOA and SA were the most accurate methods. In the last stage, machine learning was used to classify and predict the error level between the actual probability and the estimated probability based on the trained and tested data of the PDF parameters. The proposed novel methodology aims to predict the most accurate parameters, as the subsequent energy calculation phases of wind depend on the proper selection of these parameters. Hence, 24 classifier algorithms were used in this study. The medium tree classifier shows the best performance from the accuracy and training time points of view, while the ensemble-boosted trees classifier shows poor performance regarding providing correct predictions.

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48

T.N.V.L.N., KUMAR, and SATYANARAYANA R.V.S. "A MODIFIED BFOA APPROACH FOR OPTIMAL LOCATION AND SIZING OF FACTS FOR ENHANCING POWER SYSTEM SECURITY." i-manager's Journal on Power Systems Engineering 5, no. 3 (2017): 24. http://dx.doi.org/10.26634/jps.5.3.13669.

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49

Panda, Sidhartha, Narendra Kumar Yegireddy, and Sangram Keshori Mohapatra. "Hybrid BFOA–PSO approach for coordinated design of PSS and SSSC-based controller considering time delays." International Journal of Electrical Power & Energy Systems 49 (July 2013): 221–33. http://dx.doi.org/10.1016/j.ijepes.2013.01.006.

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50

Panda, Sidhartha, Sarat Chandra Swain, and Srikanta Mahapatra. "A hybrid BFOA–MOL approach for FACTS-based damping controller design using modified local input signal." International Journal of Electrical Power & Energy Systems 67 (May 2015): 238–51. http://dx.doi.org/10.1016/j.ijepes.2014.11.026.

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