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Automation, Control and Robotics Engineering (CACRE), International Conference on

作者： Mingzhu Li Xi Feng Faculty of Network Science Haikou University of Economics

ISBN: (数字)9781728198880

ISBN: (纸本)9781728198897

Due to the hysteresis and non-linearity of constant pressure water supply control system, it is difficult to realize accurate control of water supply pressure. To address this problem, an IABC-PID control algorithm based on improved artificial bee colony algorithm is proposed in this paper. To address the shortcomings of the basic artificial bee colony algorithm, which converges slowly and is prone to local optimality, a full dimensional learning strategy is introduced in the neighborhood search of the employed foragers. At the same time, Gaussian variation and chaotic perturbations are introduced to enhance the local search capability and increase the population diversity, thus speeding up the convergence speed and improving the search accuracy. The simulation results show that the IABCPID control algorithm outperforms the ABC-PID method in terms of convergence speed, search accuracy and operational stability. Compared with the response curve method, the IABC-PID algorithm has no overshoot, small adjustment time, better dynamic performance, steady-state performance and robustness. The algorithm provides a theoretical basis for real-time online PID parameter rectification of constant pressure water supply system and provides an effective means for energy saving and consumption reduction of pumps.

关键词： artificial bee algorithm Full dimensional learning strategy Gaussian variation chaotic perturbation constant pressure water supply PID parameter rectification

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An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 97卷 146-156页

作者： Tang, Qiuhua Li, Zixiang Zhang, Liping Wuhan Univ Sci & Technol Dept Ind Engn Wuhan 430081 Hubei Peoples R China

Two-sided assembly line is broadly utilized to manufacture high-volume large-size products. Reconfiguration of this line is a major practice in industry, which is known as two-sided assembly line balancing problem of type-II. To solve this NP-hard problem, this paper proposes an improved discrete artificial bee colony (DABC) algorithm via blending idle time reduction techniques. The cycle time compression mechanism speeds up the search process by reducing total idle times to a larger extent on earlier iterations. Task assignment rule is specifically designed to eliminate sequence-dependent idle times. The overload and underload are taken into account as a secondary objective to reduce the remaining idle times. To achieve a fine balance between the diversification and intensification, the employed bees in the DABC algorithm generate new food sources by combining the features of two solutions, and the onlookers expand the search space with the variable neighborhood search. Nine recent meta-heuristics including a simulated annealing algorithm and an ant colony optimization algorithm are also extended for the TALBP-II to test the performance of the proposed DABC algorithm. Experimental results demonstrate that the proposed method outperforms the nine compared algorithms and find 22 brand-new results for large-size problems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Assembly line balancing Two-sided assembly line artificial bee algorithm Idle time reduction

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artificial bee Colony algorithm for optimal Reactive Power Dispatch to Improve Voltage Stability

Artificial Bee Colony Algorithm for optimal Reactive Power D...

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

作者： Rayudu, K. Yesuratnam, G. Jayalaxmi, A. B V Raju Inst Technol Medak Telangana India JNTU Coll Engn OU Coll Engn Hyderabad Telangana India

ISBN: (纸本)9781509012770

Several traditional and artificial Intelligence techniques have been proposed for reactive power dispatch to improve voltage stability of the system. This paper presents artificial bee Colony (ABC) algorithm approach for system parameters enhancement and optimal reactive power dispatch. The transformers tap changers, Generator exciters, switchable VAR sources/Static VAR compensators (SVC) are used as control variables for improvement of system parameters and voltage stability. The proposed methodology is based on the minimization of voltage deviations at all load buses. The developed ABC algorithm is tested on an IEEE Equivalent practical Indian 24-bus system. The performance of ABC is compared with conventional Linear Programming (LP) method and results are presented for illustration purpose. The correctiveness of voltage stability is measured in terms of system parameters.

关键词： Voltage Stability Voltage Profile Improvement Reactive Power Dispatch artificial bee algorithm Linear Programming

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Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第26期83卷 67259-67281页

作者： Kilicarslan, Serhat Donmez, Emrah Bandirma Onyedi Eylul Univ Fac Engn & Nat Sci Dept Software Engn TR-10200 Balikesir Turkiye

Early diagnosis of cancer allows for easy follow-up of patients' treatment processes. The utilization of microarray gene technology has become increasingly prevalent in the detection of cancer. However, the limited success of classical data mining methods can be attributed to the absence of a linear relationship among the data in microarray datasets. artificial intelligence-based classification methods are employed to address classification challenges in datasets with a high number of attributes, due to the following reasons: This study presents a novel approach that combines adaptive particle swarm optimization (PSO) with the artificial bee colony (ABC) algorithm to effectively classify microarray datasets. Firstly, Chi2 and ANOVA F-test feature selection algorithms are applied to the microarray datasets to prevent the model from getting stuck in the local optimum. Thus, the most defining features in the dataset are selected, and the dimension is reduced. For classification success increases, we have selected optimal features with the proposed hybrid adaptive ABC + PSO, gray wolf algorithm (GWO), PSO, and ABC algorithms on the new dataset obtained. The last step is performed to classify using a support vector machine (SVM), an artificial neural network (ANN), and a nearest-neighbor (k-NN) algorithm. In the study, eight microarray datasets liver, lung, renal, pancreatic, prostate, breast, colorectal, and brain tumors were used.

关键词： Feature selection Microarray Adaptive particle swarm optimization artificial bee algorithm Classification

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bees, Bats and Glowworms: Swarm algorithms for Optimizing Industrial Plants from the Bottom-Up 15th

Bees, Bats and Glowworms: Swarm Algorithms for Optimizing In...

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15th International Conference on Agents and artificial Intelligence (ICAART)

作者： Umlauft, M. Gojkovic, M. Harshina, K. Majbour, K. Schranz, M. Lakeside Labs GmbH Klagenfurt Austria

ISBN: (纸本)9783031553257;9783031553264

The scheduling problem in production plants with diverse product ranges is NP-hard, posing challenges in finding optimal solutions. To overcome this, we propose a novel approach utilizing three bio-inspired optimization algorithms: artificial bee colony (ABC), bat, and glowworm algorithm. We apply these algorithms to address the job-shop scheduling problem and overcome computational barriers associated with traditional methods. Unlike previous studies, our approach departs from using these algorithms for global optimization within the solution space. Instead, we adopt a bottom-up strategy, directly applying them as verbatim swarm intelligence algorithms. Focusing on a semiconductor production plant, we employ agent-based modeling in the NetLogo simulation platform. By mapping bees, bats, and glowworms to plant entities like lots and machines, we establish direct correspondences. Agents interact with each other and the environment based on local rules, resulting in the emergence of desired global behavior-the industrial plant optimization. To evaluate performance, we compare our approach to a baseline algorithm employing engineered heuristics like First-In-First-Out (FIFO) and filling fullest batches first. Through this comparison, we assess the effectiveness of the bottom-up algorithms. Our results show promising performance improvements achieved with these algorithms, which rely on low-effort local calculations.

关键词： Swarm intelligence Bio-inspired algorithm artificial bee algorithm Bat algorithm Glowworm algorithm Flexible job-shop scheduling Agent-based modeling

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Multipurpose watermarking scheme via intelligent method and chaotic map

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第19期78卷 27085-27107页

作者： Lei, Baiying Zhao, Xin Lei, Haijun Ni, Dong Chen, Siping Zhou, Feng Wang, Tianfu Shenzhen Univ Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements & Ultrasoun Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Technol Shenzhen Peoples R China Georgia Inst Technol George W Woodruff Sch Mech Engn 813 Ferst Dr NW Atlanta GA 30332 USA

In this paper, we propose a novel multipurpose intelligent image watermarking scheme for both content authentication and copyright protection. To achieve this, we first utilize integer discrete wavelet transform (IDWT) for watermark insertion and detection. The low frequency component of IDWT is used to insert a robust watermark (e.g., the copyright information) for copyright protection, whereas the high frequency component of IDWT is used to embed a fragile watermark (e.g., the logo data) for content authentication. To achieve a good tradeoff among the watermark conflicting requirements (e.g., robustness, fidelity and capacity), we develop an artificial bee colony (ABC) algorithm for optimal parameter selection. Experimental results demonstrate that the proposed scheme achieves promising performance in both copyright protection and content authentication simultaneously, which confirm the superiority of our proposed algorithm as compared to existing methods.

关键词： Image watermark artificial bee algorithm Multipurpose Quantization index modulation

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Integration of Renewable Energy Distributed Generation and Battery Energy Storage in Radial Power Distribution System

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INTERNATIONAL ENERGY JOURNAL 2019年第1期19卷 27-36页

作者： Mmary, Elifuraha Reuben Marungsri, Boonruang Dar Es Salaam Inst Technol Dar Es Salaam Tanzania Suranaree Univ Technol Sch Elect Engn Nakhon Ratchasima Thailand

In the modern world power system generation, transmission, distribution and its efficient usage defines the development of the country. In distribution system of an electrical network due to the influence of increased load demand, fluctuation of voltage profile and high power losses are to be focused indeed. To avoid these technical problems in power distribution network, renewable energy distributed generation (REDG) plays a vital role in the radial distribution system. However, hybrid optimization technique which combines artificial bee colony (ABC) and Cuckoo Search (ABC/CS) algorithm are recently techniques employed to solve multi-objective problems by considering both voltage magnitude and power factor constraints. The location found by voltage stability index (VSI) which validated by ABC and sizing of REDGs found by ABC/CSA. The analysis of the present research paper guide to installation of REDGs which lead to simultaneously reduction of both active and reactive power as multi-objective functions. Simulated results such as power losses of the system and cost of REDG, cost of energy losses validated in IEEE 33-nodes and 69-nodes systems and compared with other optimization techniques reviewed in the literature.

关键词： artificial bee algorithm CSA hybrid ABC/CS renewable energy DG VSI

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A Hybrid Shuffled Frog Leaping algorithm for the Hybrid Flowshop Scheduling problem with variable processing times

A Hybrid Shuffled Frog Leaping Algorithm for the Hybrid Flow...

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第28届中国过程控制会议（CPCC 2017）暨纪念中国过程控制会议30周年

作者： Biao Zhang Quanke Pan Kunkun Peng YuHao Dai State Key Lab.of Digital Manufacturing Equipment & Technology in Huazhong University of Science & Technology

This paper studies the hybrid flowshop scheduling problem with variable processing times,which aims to minimize the objective of maximum job completion times(makespan).Additionally,the job sequence dependent machine setup times are considered *** solve this problem,a hybrid shuffled frog leaping algorithm(HSFLA) is *** this algorithm,the artificial bee algorithm(ABC) is incorporated to conduct th e evolution *** enhance the global exploration,different neighborhoods are used in different ***,a n improved shuffling process is presented to help improve the joint efforts of different neighborhoods in different subpopulati *** evaluate the performance of our proposed algorithm,the other three meta-heuristics are compared and the computational results demonstrate the effectiveness of the HSLFA.

关键词： hybrid flowshop scheduling shuffled frog leaping algorithm artificial bee algorithm Variable Processing Times

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Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2013年第3期4卷 289-300页

作者： Gurbuz, Feyza Ozturk, Celal Pardalos, Panos Univ Erciyes Dept Ind Engn TR-38039 Kayseri Turkey Univ Erciyes Dept Comp Engn TR-38039 Kayseri Turkey Univ Florida Dept Ind & Syst Engn Ctr Appl Optimizat Gainesville FL 32611 USA

Due to the worldwide growth of energy consumption, analysis of energy issues and the development of energy policy options has become an important issue. In this study, electricity energy consumption of Turkey is predicted by artificial bee colony algorithm (ABC) approaches. ABC, a recently proposed swarm based algorithm, models the intelligent foraging behavior of honey bee swarms. ABC algorithm is used to develop linear and quadratic models as well to train artificial neural network models. The proposed approaches predict Turkey's net electricity energy consumption until 2022 according to inputs from the year 1979 according to three scenarios. The data used in this study is collected from the Ministry of Energy and Natural Resources (MENR) of Turkey and Turkish Statistical Institute.

关键词： Electricity energy consumption artificial bee algorithm Prediction

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