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Strategic Bidding in Transmission Constrained Electricity Markets Using artificial bee colony algorithm

ELECTRIC POWER COMPONENTS AND SYSTEMS 2012年第16期40卷 1768-1788页

作者： Jain, A. K. Srivastava, S. C. Singh, S. N. Srivastava, L. Indian Inst Technol Kanpur Dept Elect Engn Kanpur 802016 Uttar Pradesh India Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

This article presents a methodology for a supplier's optimal bidding strategy considering network constraints and rivals' bidding behaviors. This has been formulated as a bi-level multi objective optimization problem, in which the lower-level problem represents the market clearing process by the system operator for minimization of customers' payments. The upper-level problem is a multi-objective formulation that simultaneously considers profit maximization and risk minimization. Pareto-optimal solutions of the bi-level multi-objective optimization problem have been obtained using the epsilon-constraint method. A fuzzy decision-making process has been used to derive the most efficient solution among the Pareto-optimal solutions. Upper-level objective functions are highly non-linear;therefore, the artificial bee colony algorithm, a relatively new heuristic approach, has been applied to obtain the best solution to the bi-level multi objective optimization problem for a day-ahead hourly market. The effectiveness of the proposed method has been established on a 2-bus system and the IEEE-30 bus test system. The results, obtained using the artificial bee colony algorithm, have been compared with those obtained using a genetic algorithm based approach.

关键词： artificial bee colony algorithm bi-level multi objective optimization problem congestion management epsilon-constraint method rivals' bidding behavior optimal bidding strategy

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A new artificial bee colony for vehicle routing problem with drones

A new artificial bee colony for vehicle routing problem with...

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Chinese Automation Congress (CAC)

作者： Zhengzhi Cui Deming Lei School of Automation Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665426480

Truck-drone delivery is a hybrid one combining the advantages of both vehicles including large capacity of truck and high travel speed of drone together and leads to some generalized routing problems such as vehicle routing problem with drones (VRP-D). In this paper, VRP-D is addressed and a new artificial bee colony (ABC) is applied to minimize routing cost. Two bee swarms are produced and an effective evaluation process is used to determine employed bee swarm and onlooker bee swarm dynamically. Variable neighborhood descent is constructed by using 15 neighborhood structures and adopted in employed bee phase and onlooker bee phase in different ways. Computational results reveal that the proposed ABC can obtain better solutions than the compared algorithm and has promising advantages on VRP-D.

关键词： Costs Automation Heuristic algorithms Routing artificial bee colony algorithm Drones

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Developing a Reading Concentration Monitoring System by Applying an artificial bee colony algorithm to E-Books in an Intelligent Classroom

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SENSORS 2012年第10期12卷 14158-14178页

作者： Hsu, Chia-Cheng Chen, Hsin-Chin Su, Yen-Ning Huang, Kuo-Kuang Huang, Yueh-Min Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan Natl Penghu Univ Sci & Technol Dept Informat Management Magong City 880 Penghu County Taiwan

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

关键词： reading concentration sensor technology artificial bee colony algorithm e-books intelligent classroom

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Optimizing design for continuous conversion of glycerol to 1,3-propanediol using discrete-valued optimal control

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JOURNAL OF PROCESS CONTROL 2021年 104卷 126-134页

作者： Wang, Jichao Zhang, Xin Ye, Jianxiong Wang, Juan Feng, Enmin China Univ Petr Coll Sci Qingdao 266580 Shandong Peoples R China Fujian Normal Univ Coll Math & Informat Fuzhou 350108 Fujian Peoples R China Fujian Normal Univ FJKLMAA Fuzhou 350108 Fujian Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China

This paper focuses on modeling and optimizing design for continuous conversion of glycerol to 1,3-propanediol by Klebsiella pneumoniae, wherein dilution rate of the feed medium is constantly changed with fermentation time. A nonlinear three-phase hybrid system is first established to describe this bioconversion process. With mean productivity of 1,3-propanediol as objective function and dilution rate as design variable, we then formulated the optimization design problem as a discrete-valued optimal control problem, which is eventually approximated to a large-scale parameter optimization problem via time transformation and multiple shooting approach. A multi-strategy competition artificial bee colony algorithm is developed to solve this large-scale problem. Computer simulations show the rationality of the proposed hybrid system and the feasibility of dynamic dilution rate. Numerical results indicate that, on the one hand, mean productivity in continuous culture is improved obviously using dynamic dilution rate;on the other hand, the developed numerical algorithm is efficient and valid for this complex applied optimization problem. (C) 2021 Published by Elsevier Ltd.

关键词： Discrete-valued optimal control Large-scale optimization artificial bee colony algorithm Microbial fermentation

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A modified artificial bee colony algorithm for real-parameter optimization

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INFORMATION SCIENCES 2012年 192卷 120-142页

作者： Akay, Bahriye Karaboga, Dervis Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey

Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems. The artificial bee colony algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behaviour of honey bee colonies. In this work, modified versions of the artificial bee colony algorithm are introduced and applied for efficiently solving real-parameter optimization problems. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence Self-organization artificial bee colony algorithm Real-parameter optimization

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artificial bee colony optimized random forest model for prediction of fly ash concrete compressive strength

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MethodsX 2025年 14卷

作者： Bali, Manish Mishra, Ved Prakash Yenkikar, Anuradha School of Engineering Amity University Dubai Campus Dubai 25314 United Arab Emirates Department of CSE (AI) Vishwakarma Institute of Technology Maharashtra Pune 411048 India

Concrete compressive strength (CS) is a vital parameter in structural engineering, yet traditional evaluation methods are time-consuming, destructive, and resource intensive. This study presents a data-driven, computational approach for predicting the CS of fly ash concrete (FAC) using a hybrid artificial bee colony (ABC)-optimized Random Forest (RF) model. The experimental dataset consists of concrete mix designs with varying proportions of fly ash, steel fibers, and water-to-binder ratios, tested over multiple curing ages. Among the regression models, RF algorithm demonstrating the highest predictive accuracy was further improved by tuning its hyperparameters through ABC optimization. The proposed ABC-RF model achieved an R² of 0.95 and outperformed several state-of-the-art deep learning models. The model also identified that a 30 % fly ash replacement yields optimal compressive strength, a finding further supported by SEM analysis, which showed dense C-S-H matrix formation at this level. The study demonstrates the effectiveness of bio-inspired optimization in civil material modelling and contributes toward sustainable construction practices. The method involves: • Experimental dataset generated from 168 fly ash concrete mix designs with varied input proportions. • Five regression models trained and evaluated using RMSE, MAE, and R². • Hyperparameters of the RF model optimized using the artificial bee colony algorithm.

关键词： artificial bee colony algorithm Concrete compressive strength Fly ash concrete Machine learning Metaheuristic optimization

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Methanol futures hedging with skewed normal distribution by copula method

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2021年第7期98卷 1327-1348页

作者： Yu, Xing Wang, Xinxin Zhang, Weiguo Zheng, Chengli Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Peoples R China South China Univ Technol Sch Business Adm Guangzhou Peoples R China

As an environmental protection fuel, methanol is widely used in national economy. The tremendous price fluctuation of methanol makes risk avoidance an important issue. However, traditional normal hypothesis in the existing literature underestimates the potential risk and leads to an inefficient hedging strategy, so we studied hedging strategy with methanol futures contracts based on the skewed normal hypothesis. Considering that copula methods allow us to construct a flexible multivariate distribution when solving the problem of asymmetry and nonlinearity, the dependence structure between spot and futures return is modelled through copula functions in this paper. Since likelihood equations do not have explicit solutions in the context of skewed normal, Genetic algorithm is used to estimate the parameters of a skew normal distribution. To deal with the complexity of the proposed model, the artificial bee colony algorithm is adopted to search for the optimal solutions. Empirical results show that skewed normal distribution can represent the distribution characteristics of return better and improve the hedging effectiveness. Gaussian copula describes the dependence structure of spot and futures quite well. The algorithms designed to obtain the parameters in the marginal distributions and to find the optimal hedge ratio are effective and feasible.

关键词： Futures hedging skewed normal distribution methanol price copula method genetic algorithm artificial bee colony algorithm

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ABC algorithm based optimal sizing and placement of DGs in distribution networks considering multiple objectives

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AIN SHAMS ENGINEERING JOURNAL 2021年第1期12卷 697-708页

作者： Al-Ammar, Essam A. Farzana, Kiran Waqar, Asad Aamir, Muhammad Saifullah Ul Haq, Azhar Zahid, Muhammad Batool, Memoona King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Bahria Univ Dept Elect Engn Islamabad 44000 Pakistan PAF IAST Dept Elect & Comp Engn Haripur 22620 Pakistan Univ Engn & Technol Dept Ind Engn Taxila 47050 Pakistan Natl Univ Sci & Technol Coll Elect & Mech Engn Rawalpindi 44000 Pakistan Riphah Int Univ Dept Elect Engn Lahore 55150 Pakistan

In recent years, due to increase in load demand, distribution networks have got substantial attention to get optimized. It is significant to optimally place the distributed generators (DGs) in distribution networks to minimize power losses and voltage drops. However, the DGs incur certain investment and operational costs, and their placement is only viable if these costs overcome energy losses. Therefore, current paper investigates the optimal sizing and placement of DGs in distribution networks with a novel concept to simultaneously minimize total energy cost along with total power loss and average voltage drop. artificial bee colony (ABC) algorithm is proposed to solve the considered multi-objective problem. The performance of the proposed ABC algorithm is tested with standard algorithms. Newton Raphson load flow (NRLF) analysis is conducted on IEEE 33 and 69-bus radial networks and on CIGRE medium voltage (MV) benchmark grid. Two test cases have been formed and investigated. The results prove that proposed ABC algorithm mostly outperforms other algorithms. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Distributed generation (DG) Newton Raphson Load flow (NRLF) analysis Optimal sizing and placement of DGs Line loss Voltage drop artificial bee colony algorithm

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Flexible flow shop scheduling with interval grey processing time

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GREY SYSTEMS-THEORY AND APPLICATION 2021年第4期11卷 779-795页

作者： Wang, Yuquan Xie, Naiming Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China

Purpose - purpose of this paper is providing a solution for flexible flow shop scheduling problem with uncertain processing time in aeronautical composite lay-up workshop. Design/methodology/approach - A flexible flow scheduling model and algorithm with interval grey processing time is established. First, according to actual needs of composite laminate shop scheduling process, interval grey number is used to represent uncertain processing time, and interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart, and other methods are further applied. Then a flexible flow shop scheduling model with interval grey processing time (G-FFSP) is established, and an artificial bee colony algorithm based on an adaptive neighbourhood search strategy is designed to solve the model. Finally, six examples are generated for simulation scheduling, and the efficiency and performance of the model and algorithm are evaluated by comparing the results. Findings - Results show that flexible flow shop scheduling model and algorithm with interval grey processing time can provide an optimal solution for composite lay-up shop scheduling problems and other similar flow shop scheduling problems. Social implications - Uncertain processing time is common in flexible workshop manufacturing, and manual scheduling greatly restricts the production efficiency of workshop. In this paper, combined with grey system theory, an intelligent algorithm is used to solve flexible flow shop scheduling problem to promote intelligent and efficient production of enterprises. Originality/value - This paper applies and perfects interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart and other methods. Aflexible flow shop scheduling model with interval grey processing time is established, and an artificial bee colony algorithm with an adaptive domain search strategy is designed. It provides a comprehensive solut

关键词： Grey system theory Flexible flow shop scheduling Uncertain processing time artificial bee colony algorithm

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ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

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PHYSIOLOGICAL MEASUREMENT 2024年第5期45卷 055011-055011页

作者： Kasmaee, Adele Mirzaee Moghaddam Ataei, Alireza Moravvej, Seyed Vahid Alizadehsani, Roohallah Gorriz, Juan M. Zhang, Yu-Dong Tan, Ru-San Acharya, U. Rajendra Univ Guilan Fac Math Sci Dept Pure Math Rasht Iran Persian Gulf Univ Fac Intelligent Syst Engn & Data Sci Dept Math Bushehr *** Iran Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Waurn Ponds Australia Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Univ Leicester Dept Informat Leicester England Duke NUS Med Sch Singapore Singapore Univ Southern Queensland Sch Math Phys & Comp Springfield Australia

Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities. Approach. This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the artificial bee colony (ABC) algorithm to enhance the starting point for learning. An array of convolutional neural networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process. Main results. ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2% and a geometric mean of 90.6%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs. Significance. The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

关键词： myocarditis imbalanced classification ensemble convolutional neural network reinforcement learning artificial bee colony algorithm

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