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Security Constraint Unit Commitment on Combined Solar Thermal Generating Units using abc algorithm

INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2016年第4期6卷 1361-1372页

作者： Sreejith, S. Gandhi, Indra Samiappan, Dhanalakshmi Muruganandam, M. VIT Univ Sch Elect Engn Vellore Tamil Nadu India SRM Univ Dept ECE Madras Tamil Nadu India Wollega Univ Dept Elect & Comp Engn Nekemte Ethiopia

In this paper, Security Constrained Unit Commitment (SCUC) problem with combined Thermal-Solar generating units is solved using Artificial Bee Colony (abc) algorithm. SCUC problem aims to obtain minimum generation cost while satisfying the system, network and security constraints. The SCUC problem is divided into Unit Commitment Problem (UCP) which is considered as the master problem and Security Constrained Economic Dispatch (SCED) is considered as the sub problem. Here, both thermal and solar generations are considered in UCP and SCED solutions. Binary Coded abc algorithm is used to solve the UCP and Real Coded abc algorithm is used for solving the SCED problem. The constraints such as spinning reserves, hourly load demands, ramp up/ramp down limits of generators, minimum up/down limits, line flow and voltage limits are considered in the problem formulation. Beta distribution function is employed to forecast the solar power generation in different seasons. The comparison is carried out with and without incorporating solar generation. The proposed method is validated on a 6 bus system and a South Indian 86 bus utility.

关键词： Solar farm Security Constraints Unit Commitment Beta distribution function abc algorithm

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Application of an Improved abc algorithm in Urban Land Use Prediction

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INFORMATION 2018年第8期9卷 193页

作者： Huo, Jiuyuan Zhang, Zheng Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Gansu Peoples R China

Scientifically and rationally analyzing the characteristics of land use evolution and exploring future trends in land use changes can provide the scientific reference basis for the rational development and utilization of regional land resources and sustainable economic development. In this paper, an improved hybrid artificial bee colony (abc) algorithm based on the mutation of inferior solutions (MHabc) is introduced to combine with the cellular automata (CA) model to implement a new CA rule mining algorithm (MHabc-CA). To verify the capabilities of this algorithm, remote sensing data of three stages, 2005, 2010, and 2015, are adopted to dynamically simulate urban development of Dengzhou city in Henan province, China, using the MHabc-CA algorithm. The comprehensive validation and analysis of the simulation results are performed by two aspects of comparison, the visual features of urban land use types and the quantification analysis of simulation accuracy. Compared with a cellular automata model based on a particle swarm optimization (PSO-CA) algorithm, the experimental results demonstrate the effectiveness of the MHabc-CA algorithm in the prediction field of urban land use changes.

关键词： abc algorithm cellular automata rule mining land use change prediction

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A Circular Electronic Elements Localization Method Based on abc algorithm

A Circular Electronic Elements Localization Method Based on ...

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第37届中国控制会议

作者： Xin-yu Liu Na Dong Jian-fang Chang Dong-hui Li Ai-guo Wu Academy of Electrical and Information Engineering Tianjin University

In PCB circuit detection system, it is necessary to locate and measure elements accurately. For the circular electronic elements in PCB, this paper proposes a localization method based on artificial bee colony（abc） algorithm. In this method, the three-point method is introduced to correspond a circle with an individual in bee colony, so that the circles are located though bee colony’s search. The first step of circle localization method is extracting the edge in PCB images with the canny operator, then the abc algorithm is used to search the circles. The mean gray values of the candidate circles are defined as fitness *** the guidance of the fitness function, the proposed method can locate an optimal circle in an iteration. In every iteration,the method needs delete the already located circles to prevent that the circles are located repeatedly until all circular electronic elements are located. Experiments show that the method can locate the circular electronic elements with shorter execution time and higher accuracy to meet industrial needs.

关键词： Circle Localization abc algorithm Three-point Method Canny Operator PCB Circular Electronic Elements

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Performance analysis of artificial bee colony (abc) algorithm in optimizing release policy of Aswan High Dam

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NEURAL COMPUTING & APPLICATIONS 2014年第5期24卷 1199-1206页

作者： Hossain, Md. S. El-shafie, A. UKM Dept Civil & Struct Engn Bangi Malaysia

The paper presents a study on developing an effective reservoir operation policy by using artificial bee colony (abc) algorithm. The decision maker of a reservoir system always needs a guideline to operate the reservoir in an optimal way. Such guidelines named 'release curves' have developed for high-, medium-, and low-inflow category that can answer how much water needs to be released for a month by observing the reservoir level (storage condition). The Aswan High Dam of Egypt has been considered for the case study. For comparing the model efficiency, another heuristic approach-genetic algorithm (GA)-has been used. So far, GA is well established and most popular in reservoir release optimization. Historical inflow data for 18 years have been used for simulation purpose, and the general system performance-measuring indices (such as reliability, resiliency, and vulnerability) have been measured. The application procedure and problem formulation of abc are very simple and can be used in optimizing reservoir system. After using the actual historical inflow, the release policy succeeded in meeting demand for about 98 % of the total time period. According to the simulation results, abc algorithm showed better performance than the GA approach in reservoir release optimization.

关键词： Optimization abc algorithm GA Reservoir-operating policy

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A comparative study between artificial bee colony (abc) algorithm and its variants on big data optimization

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MEMETIC COMPUTING 2020年第2期12卷 129-150页

作者： Aslan, Selcuk Ondokuz Mayis Univ Samsun Turkey

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (abc) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard abc algorithm and its well-known variants including the gbest-guided abc algorithm, the differential evolution based abc/best/1 and abc/best/2 algorithms, crossover abc algorithm, converge-onlookers abc algorithm and quick abc algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard abc algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.

关键词： abc algorithm Big data optimization Signal decomposition

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CASCADED abc-LM algorithm BASED OPTIMIZATION AND NONLINEAR SYSTEM IDENTIFICATION

CASCADED ABC-LM ALGORITHM BASED OPTIMIZATION AND NONLINEAR S...

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10th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Dilmen, Erdem Yilmaz, Selim Beyhan, Selami Pamukkale Univ Dept Mechatron Engn TR-20070 Denizli Turkey Pamukkale Univ Dept Elect & Elect Engn TR-20070 Denizli Turkey

ISBN: (纸本)9781479933433

In this paper, the well-known heuristic Artificial Bee Colony algorithm (abc) and deterministic Levenberg-Marquardt (LM) optimization method are unified to get better performance of nonlinear optimization. In the proposed cascaded abc-LM algorithm, the power of the abc and LM algorithms are synergized to reduce computational-time and get rid of the problem "stucking at local minima" of some nonlinear functions. Then, the proved power of the cascaded optimization is also tested on the training of Artificial Neural Network (ANN) for classification of XOR data and nonlinear system identification of real-time inverted pendulum set-up. The comparisons in function optimization and system identification using abc, LM and abc-LM showed that abc-LM optimized nonlinear functions and abc-LM trained ANN has resulted smaller cost functions and mean-squared-error (MSE) values, respectively.

关键词： abc algorithm LM method nonlinear function optimization nonlinear system identification

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Designing of Interacting RNAs using Artificial Bee Colony (abc) algorithm 8

Designing of Interacting RNAs using Artificial Bee Colony (A...

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8th Biomedical Engineering International Conference (BMEiCON)

作者： Thaiprasit, Jittrawan Kaewkamnerdpong, Boonserm Chatphakawan, Phanarin Ruangkae, Nipapat Yamsin, Pornpan Leelachuthipong, Nutthaphong Meechai, Asawin King Mongkuts Univ Technol Thonburi Fac Engn Biol Engn Program Bangkok Thailand King Mongkuts Univ Technol Thonburi Fac Engn Dept Chem Engn Bangkok Thailand

ISBN: (纸本)9781467391580

One of polymeric biological molecules called ribonucleic acid (RNA) is involved in diverse biological roles in regulation of gene expression. Generally, secondary structures of RNA determine their functions in living organisms. In medical field, various structures of synthetic RNAs are much needed at present. Recently, an RNA design tool has been developed to identify optimal RNA sequences for the desired interacting RNAs based on Genetic algorithm (GA). In an attempt to improve the design tool, we applied Artificial Bee Colony (abc) algorithm, which can outperform several population-based methods including GA. To achieve this goal, we developed an abc-based RNA design tool and employed this tool to design 6 different interacting RNA models. The design performance from abc based tool for all models were compared with that from the existing GA-based tool. It was found that the abc-based tool and the GA-based tools equally perform in all 6 models. Both tools can achieve the fitness score above 0.996. Although the abc-based tool could be used to design interacting RNA more reliably, it seems to require more design time, especially in the models with larger RNA length. Thus, with further improvement, this tool shall he a promising tool for designing synthetic RNA devices for biomedical applications.

关键词： RNA RNA-RNA interaction abc algorithm optimization

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Efficient Localization in Edge Detection by Adapting Articial Bee Colony (abc) algorithm 7th

Efficient Localization in Edge Detection by Adapting Articia...

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International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Vasquez F, Jaime Contreras A, Ricardo Pinninghoff J, M. Angelica Univ Concepcion Dept Comp Sci Concepcion Chile

ISBN: (纸本)9783319597409;9783319597393

The problem of edge detection considers two stages: localization and identification, where localization is the search of pixels in an image and identification is the process of deciding if a pixel belongs, or not, to an edge. The Canny edge detector has an effective identification involving the analysis of every pixel that belongs to an image. On the other side, artificial bee colony (abc) algorithm simulates the foraging behavior of honey bees, doing an efficient search of food sources. In this proposal, abc algorithm and Canny are integrated to create abc-ED, an efficient edge detector algorithm, that does not require to analyze all the pixels of an image to detect its edges. The dataset BSDS500 was used for experimentation, and results show that it is not necessary to analyze every pixel in the image to detect the same edges detected when using Canny.

关键词： abc-ED abc algorithm Edge detection Canny edge detector

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Performance Optimization of Axial-flow Hydraulic Turbine Using Artificial Bee Colony (abc) algorithm

Performance Optimization of Axial-flow Hydraulic Turbine Usi...

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International Conference on Sustainable Energy Engineering and Application (ICSEEA) - Continuous Improvement of Sustainable Energy for Eco-Mobility

作者： Soesanto, Qidun Maulana Binu Susatyo, Anjar Widiyanto, Puji Indonesian Inst Sci Res Ctr Elect Power & Mechatron Bandung Indonesia

ISBN: (纸本)9781538617656

This paper presents the numerical optimization of axial-flow hydraulic turbine performance. The turbine was designed to operate at a net head of 0.9 meters, a rotational speed of 500 rpm, and maximum flow rates are 0.12 m(3)/s. The initial turbine design was optimized by using shock-free inflow criterion to minimize the hydrofoil losses at the turbine cascades. Potential flow of the hydrofoil was analyzed using surface vorticity model with modified coupling coefficient to deal with turbomachinery blade cascades. The abc algorithm was used to arrange the geometry of guide vane's and runner blade's cascades to meet the shock-free inflow criterion. In the present study, the cascade geometry optimization of the axial-flow hydraulic turbine is divided into two main steps. Firstly, cascade at each section of guide vane was optimized by arranging two design variables, i.e. stagger angle and chamber line of the hydrodynamic profile. Secondly, cascade at each section of runner blade was optimized by arranging one design variable, i.e., stagger angle. Both steps have the same objective function that minimizes the difference between the design of inlet flow angles and shock-free inflow angles. The optimization results showed that the abc algorithm performed very well, the average of best values at each section was an order of magnitude of -14. The performance of turbines was predicted using computational fluid dynamics (CFD) approach based on finite volume method. The numerical results showed that the optimization of two-dimensional cascades geometries by using shock-free inflow criterion successfully improved the performance of initial design of axial-flow hydraulic turbine.

关键词： axial-flow hydraulic turbine shock-free inflow criterion abc algorithm CFD

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Crop Classification Using Artificial Bee Colony (abc) algorithm

Crop Classification Using Artificial Bee Colony (ABC) Algori...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Vazquez, Roberto A. Garro, Beatriz A. La Salle Univ Fac Ingn Intelligent Syst Grp Benjamin Franklin 47 Mexico City 06140 DF Mexico IIMAS UNAM Ciudad Univ Mexico City DF Mexico

ISBN: (纸本)9783319410098;9783319410081

Identifying which crop is growing in certain areas is important to many national and multinational agricultural agencies for forecasting grain supplies, monitoring farming activity, facilitating crop rotation records, etc. In order to achieve that, the agencies require to schedule censuses on a regular basis. Recently, different techniques based on remote sensing have been applied to collect the information and perform a crop classification task. In this paper, we described a methodology to perform a crop classification task based on the Gray Level Co-Occurrence Matrix (GLCM) and the artificial bee colony (abc) algorithm. The proposed methodology selects the set of features from the GLCM that allow classify the crops with a good accuracy using the abc algorithm in terms of a distance classifier. The accuracy of the proposed methodology was tested over a specific region of Mexico and compared against different distance classifiers.

关键词： Crop classification abc algorithm Feature selection

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