Gotowe bibliografie tematyczne / Artificial bee colony optimization (ABCO) algorithm and bacterial foraging optimization (BFO) technique

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Gotowa bibliografia na temat „Artificial bee colony optimization (ABCO) algorithm and bacterial foraging optimization (BFO) technique”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Artificial bee colony optimization (ABCO) algorithm and bacterial foraging optimization (BFO) technique"

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J., A. Baskar* Dr. R. Hariprakash (IITM) Dr. M. Vijayakumar. "COMPARISON OF BACTERIAL FORAGING OPTIMIZATION AND ARTIFICIAL BEE COLONY OPTIMIZATION TECHNIQUE FOR DISTRIBUTED GENERATION SIZING AND PLACEMENT IN AN ELECTRICAL DISTRIBUTION SYSTEM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 1–14. https://doi.org/10.5281/zenodo.802775.

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Integration of Distributed Generation (DG) in an electrical distribution system has increased recently due to voltage improvement, line loss reduction, environmental advantages, and postponement of system upgrading, and increasing reliability. Improper location and capacity of DG may affect the voltage stability on the Distribution System (DS). Optimization techniques are tools used to predict size and locate the DG units in the system, so as to utilize these units optimally within certain limits and constraints. The DG units’ sizing and placement is formulated using mixed-integer nonlinear pr
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S, Kalaivani, and Gopinath G. "MODIFIED BEE COLONY WITH BACTERIAL FORAGING OPTIMIZATION BASED HYBRID FEATURE SELECTION TECHNIQUE FOR INTRUSION DETECTION SYSTEM CLASSIFIER MODEL." ICTACT Journal on Soft Computing 10, no. 4 (2020): 2146–52. https://doi.org/10.21917/ijsc.2020.0305.

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Feature selection (FS) plays an essential role in creating machine learning models. The unrelated characteristics of the data disturb the precision of the perfection and upsurges the training time required to build the model. FS is a significant process in creating the Intrusion Detection System (IDS). In this document, we propose a technique for selecting container functions for IDS. To develop the performance capacity of the modified Artificial Bee Colony (ABC) procedure, a hybrid method is presented in which the swarm behavior of the Bacterial Foraging Optimization (BFO) algorithm is entere
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Pan, Yaxi, and Jian Dong. "Design and Optimization of an Ultrathin and Broadband Polarization-Insensitive Fractal FSS Using the Improved Bacteria Foraging Optimization Algorithm and Curve Fitting." Nanomaterials 13, no. 1 (2023): 191. http://dx.doi.org/10.3390/nano13010191.

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A frequency-selective surface (FSS) optimization method combining a curve-fitting technique and an improved bacterial foraging optimization (IBFO) algorithm is proposed. In the method, novel Koch curve-like FSS and Minkowski fractal islands FSS were designed with a desired resonance center frequency and bandwidth. The bacteria foraging optimization (BFO) algorithm is improved to enhance the performance of the FSS. A curve-fitting technique is provided to allow an intuitive and numerical analysis of the correspondence between the FSS structural parameters and the frequency response. The curve-f
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Abdulsaheb, Jaafar Ahmed, and Dheyaa Jasim Kadhim. "Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey." Robotics 12, no. 4 (2023): 93. http://dx.doi.org/10.3390/robotics12040093.

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The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decompos
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Ouadfel, Salima, and Abdelmalik Taleb-Ahmed. "Performance Study of Harmony Search Algorithm for Multilevel Thresholding." Journal of Intelligent Systems 25, no. 4 (2016): 473–513. http://dx.doi.org/10.1515/jisys-2014-0147.

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AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel
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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 (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 ord
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Nayak, Smrutiranjan, Sanjeeb Kumar Kar, and Subhransu Sekhar Dash. "Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 12. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp12-21.

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In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing
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Nayak, Smrutiranjan, Sanjeeb Kumar Kar, and Subhransu Sekhar Dash. "Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 12–21. https://doi.org/10.11591/ijeecs.v24.i1.pp12-21.

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In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing
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Badriyah, Tessy, Iwan Syarif, and Fitriani Rohmah Hardiyanti. "Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024). http://dx.doi.org/10.62527/joiv.8.1.2149.

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High-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature sel
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Manoharan, Hariprabhu, Sundararaju Karuppannan, Kumar Chandrasekaran, and Sourav Barua. "Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique." IET Renewable Power Generation, October 18, 2024. http://dx.doi.org/10.1049/rpg2.13128.

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AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller par
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Części książek na temat "Artificial bee colony optimization (ABCO) algorithm and bacterial foraging optimization (BFO) technique"

1

Sakulin, Sergey, Alexander Alfimtsev, and Yuri Kalgin. "Nature-Inspired Usability Optimization." In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3222-5.ch007.

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Nature-inspired algorithms have come into use to solve more and more optimization tasks of high dimension when classical optimization algorithms do not apply. The task of user interface usability optimization becomes the one to be solved by nature-inspired algorithms. Usability optimization suggests a choice of interface design out of a large number of variants. At that, there is no common technique to determine the objective function of such optimization that would lead to the invitation of highly qualified specialists to implement it. The chapter presents a new approach of automatic interfac
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