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Two phases of metaheuristic techniques for the minimum conflict weighted spanning tree problem

APPLIED SOFT COMPUTING 2023年 138卷

作者： Chaubey, Punit Kumar Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 India

The minimum conflict weighted spanning tree (MCWST) problem is a variant of spanning tree problem with conflicting edge pairs whose primary goal is to find a spanning tree (T) that contains the minimum number of conflicting edge-pairs, and the secondary goal is to minimize the weight of T, iff T is a conflict free spanning tree. Being NP-hard, very few studies have been carried out in the domain of metaheuristic techniques. This paper develops an approach consisting of two phases of metaheuristic techniques (TPMT) in order to tackle the two goals of this problem, where a hybrid artificial bee colony algorithm (hABC) is used as the first phase of TPMT to tackle the first goal of the problem, and as soon as hABC returns a conflict free solution, an iterated local search (ILS) that starts with this conflict free solution is used as the second phase to tackle the second goal. The components of hABC such as neighborhood operator, local search and operator in scout bee phase coordinate effectively in finding a spanning tree with minimum number of conflicts, whereas the components of ILS such as perturbation procedures, local seas and strong perturbation integrated with memory in acceptance criterion coordinate effectively in finding a minimum cost conflict free spanning tree based on the initial conflict free solution returned by hABC. Experimental results on available two types of benchmark instances (type 1 and type 2) show that the proposed TPMT overall is superior to state-of-the-art approaches, not only in terms of solution quality, but also in terms of computational time. Also, TPMT finds some improved results.

关键词： Metaheuristic artificial bee colony algorithm Iterated local search Conflict Spanning tree

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Optimization using artificial bee colony based clustering approach for big data

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 12169-12177页

作者： Ilango, S. Sudhakar Vimal, S. Kaliappan, M. Subbulakshmi, P. Sri Krishna Coll Engn & Technol Dept CSE Coimbatore 641008 Tamil Nadu India Natl Engn Coll Dept IT Kovilpatti 628503 India Hindustan Inst Technol & Sci Sch Comp Sci Dept CSE Chennai 603103 Tamil Nadu India

As one of the major problems is that the time taken for executing the traditional algorithm is larger and that it is very difficult for processing large amount of data. Clusters possess high degree of similarity among each cluster and have low degree of similarity among other clusters. Optimization algorithm for clustering is the art of allocating scarce resources to the best possible effect. The traditional optimization algorithm is not suitable for processing high dimensional data. The main objective of proposed artificial bee colony (ABC) approach is to minimize the execution time and to optimize the best cluster for the various sizes of the dataset. To deal with this, we are normalizing to distributed environment for time efficiency and accuracy. The proposed ABC algorithm simulates the behavior of real bees for solving numerical optimization problems particularly in clustering. The dataset size is varied for the algorithm and is mapped with its appropriate timings. The result is observed for various fitness and probability value which is obtained from the employed and the onlooker phase of ABC algorithm from which the further calibrations of classification error percentage is done. The proposed ABC algorithm is implemented in Hadoop environment using mapper and reducer programming. An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters. The results show that the proposed ABC scheme gives a better performance than PSO and DE in terms of time efficiency.

关键词： artificial bee colony algorithm Big data Cluster Hadoop Optimization

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Variation Model Abstraction and Adaptive Control Based on Element Description Method Toward Smart Factory

IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY

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IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY 2021年 2卷 489-497页

作者： Takeuchi, Issei Katsura, Seiichiro Tokyo Automat Machinery Works Ltd Adv Technol Res Div Tokyo 1010032 Japan Keio Univ Dept Syst Design Engn Yokohama Kanagawa 2238522 Japan

In this study, a method for realizing an intelligent production process by reducing quality variation in the manufacturing industry is proposed. A quality fluctuation model in a production process is abstracted, and the quality is improved using adaptation rules based on the model. In this framework, the value directly related to product quality is expressed as multiplying the coefficient and the setting parameter. This expression makes it possible to regard the quality variations as being caused by the coefficient variations. Hence, it is possible to reduce the variation of quality by predicting the fluctuation of the coefficient from various data acquired from the production line and increasing or decreasing the setting parameter based on the predicted value. Moreover, the element description method is applied to predict the fluctuation of the coefficient. The element description method has the advantages of a model-based method whose physical meaning can be understood and the advantages of a database method applicable to an unknown system. Therefore, the mechanism of fluctuation can be abstracted and can be used as explicit knowledge. In this study, this framework is applied to reduce the variation in filling weight of the powder filling process and is demonstrated. As a result, the filling weight variation has been reduced by approximately 33%.

关键词： Powders Industrial electronics Adaptation models Smart manufacturing Production Mathematical models artificial bee colony algorithm artificial bee colony algorithm artificial intelligence element description method neural network smart factory

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Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

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JOURNAL OF VIBROENGINEERING 2019年第1期21卷 111-132页

作者： Bai, Liang Feng, Yun-Wen Li, Ning Xue, Xiao-Feng Northwestern Polytech Univ Sch Aeronaut Western Youyi St 127 Xian 710072 Shaanxi Peoples R China Northeastern Univ Coll Sci Shenyang 110819 Liaoning Peoples R China

Combining P-type iterative learning (IL) control, fuzzy logic control and artificial bee colony (ABC) algorithm, a new optimal fuzzy IL controller is designed for active vibration control of piezoelectric smart structures. In order to accelerate the learning speed of feedback gain, the fuzzy logic controller is integrated into the ANSYS finite element (FE) models by using APDL (ANSYS Parameter Design Language) approach to adjust adaptively the learning gain of P-type IL control. For improving the performance and robustness of the fuzzy logic controller as well as diminishing human intervention in the operation process, ABC algorithm is used to automatically identify the optimal configurations for values in fuzzy query table, fuzzification parameters and defuzzification parameters, and the main program of ABC algorithm is operated in MATLAB. The active vibration equations are driven from the FE equations for the dynamic response of a linear elastic piezoelectric smart structure. Considering the vibrations generated by various external disturbances, the optimal fuzzy IL controller is numerically investigated for a clamped piezoelectric smart plate. Results demonstrate that the proposed control approach makes the feedback gain has a fast learning speed and performs excellent in vibration suppression. This is demonstrated in the results by comparing the new control approach with the P-type IL control.

关键词： P-type IL control fuzzy logic control artificial bee colony algorithm active vibration control piezoelectric smart structure

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Sparse signal reconstruction by swarm intelligence algorithms

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2021年第2期24卷 319-330页

作者： Erkoc, Murat Emre Karaboga, Nurhan Erciyes Univ Elect & Elect Eng Dep TR-38039 Kayseri Turkey

This study introduces a new technique for sparse signal reconstruction. In general, there are two classes of algorithms in the recovery of sparse signals: greedy approaches and l(1)-minimization methods. The proposed method employs swarm intelligence based techniques for sparse signal reconstruction. With this technique, the proposed method tries to find nonzero entries of a sparse signal. In addition, it uses least square method to obtain the magnitude of the reconstructed signal. In this study, artificial bee colony and particle swarm optimization algorithms are employed for the reconstruction of sparse signals. The algorithms are tested on some benchmark problems and empirical results for a number of test cases are obtained. In addition, the reconstruction performances of these two algorithms are compared with l(1) minimization, greedy algorithms and several lately announced methods. According to the results of the tests and comparisons, the proposed method for artificial bee colony and particle swarm optimization algorithms have better performance than the classical methods for some test cases and it can be used to recover sparse signals in general. Additionally, this method can be used to recover a sparse signal when classical methods fail to reconstruct the signal for some cases. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

关键词： Compressive sensing Sparse signal reconstruction Swarm intelligence algorithms artificial bee colony algorithm Particle swarm optimization

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A personalized auxiliary material recommendation system based on learning style on Facebook applying an artificial bee colony algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第5期64卷 1506-1513页

作者： Hsu, Chia-Cheng Chen, Hsin-Chin Huang, Kuo-Kuang Huang, Yueh-Min Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan Chia Nan Univ Pharm & Sci Dept Appl Geoinformat Tainan 717 Taiwan

Facebook is currently the most popular social networking site in the world, providing an interactive platform that enables users to contact friends and other social groups, as well as post a large number of photos, videos, and links. Recently, many studies have investigated the effects of using Facebook on various aspects of education, and it has been used as a learning platform for sharing auxiliary materials. However, not all of the auxiliary materials posted may conform to the individual learning styles and abilities of each user. This study thus proposes a personalized auxiliary material recommendation system based on the degree of difficulty of the auxiliary materials, individual learning styles, and the specific course topics. An artificial bee colony algorithm is implemented to optimize the system. The results indicate that this method is superior to other schemes, and improves the execution time and accuracy of the recommendation system in an efficient manner. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Learning style Facebook Auxiliary material artificial bee colony algorithm

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Wireless Sensor Network Node Localization algorithm Based on PSO-MA

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JOURNAL OF WEB ENGINEERING 2021年第4期20卷 1075-1092页

作者： Liu, Wenli Shi, Cuiping Zhu, Hengjun Yu, Hongbo Qiqihar Univ Qiqihar 161006 Heilongjiang Peoples R China

Aiming at the large error and low accuracy of wireless sensor node location, this paper proposes a node location method based on the fusion of Particle Swarm Optimization and Monkey algorithm (PSO-MA). Firstly, this article describes the node location model based on DV-HOP algorithm;secondly, this article uses PSO in node location, uses place Laplace distribution for population initialization, improves population diversity, and optimizes particle weights to avoid algorithm falling into local optimality. In this paper, dynamic guidance factors are used to update individual positions to improve individual optimization capabilities, and Monkey algorithm is used to select individuals to improve the quality of optimal solutions. In the simulation experiment, the algorithm PSO and MA of this paper are compared to achieve better positioning results in the reference node ratio, node density and communication radius indicators.

关键词： Particle swarm optimization artificial bee colony algorithm node location

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Optimized Scheduling for Microgrid-Connected Battery Swapping Stations Using Modified Particle Swarm Optimization

Optimized Scheduling for Microgrid-Connected Battery Swappin...

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International Conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Arpon Bose Dibya Sandip Kumer Sarker Tanvir Ahmed Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The rapid adoption of Electric Vehicles (EVs) is creating unprecedented demand on power grids, necessitating innovative strategies for energy management. Battery Swapping Stations (BSS) present an efficient alternative to traditional EV charging, enabling quick battery replacement while also functioning as energy storage assets within Distributed Energy Resource (DER) based microgrids. This study explores the integration of BSS into microgrid systems, focusing on optimizing charging and discharging schedules to maximize profitability for microgrid operators. A Modified Particle Swarm Optimization (MPSO) algorithm is proposed, featuring dynamic decision boundary adjustments to enhance power flow management. The study compares the performance of the MPSO algorithm with the artificial bee colony (ABC) algorithm in both Battery-to-Grid (B2G) and non-B2G scenarios. Results reveal that the MPSO algorithm significantly improves power flow optimization, leading to a $27 increase in daily profits for microgrid operators. These findings underscore the potential of advanced BSS scheduling techniques to enhance microgrid efficiency, support renewable energy integration, and improve overall economic outcomes for operators.

关键词： Renewable energy sources Heuristic algorithms Microgrids Optimal scheduling artificial bee colony algorithm Batteries Distributed power generation Particle swarm optimization Vehicle dynamics Load flow

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Hybrid Taguchi-chaos of multilevel immune and the artificial bee colony algorithm for parameter identification of chaotic systems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第5期64卷 1108-1119页

作者： Tien, Jia-Ping Li, Tzuu-Hseng S. Natl Cheng Kung Univ AiRobots Lab Dept Elect Engn Energy Technol & Strategy Ctr Tainan 701 Taiwan

In this paper, a novel evolutionary learning algorithm is proposed by hybridizing the Taguchi method, chaos disturbance operation, multilevel immune algorithm (MIA), and artificial bee colony algorithm (ABC). The algorithm is thus called HTCMIABC to estimate the parameter of chaotic systems. The HTCMIABC comprises two main different phases. First, we use the MIA as the recognition phase to balance local and global searches and accelerate the search speed to enhance the evolutionary phase. Second, the evolutionary phase is built on the ABC and chaos disturbance operation to have the capabilities of exploration and exploitation. Moreover, the Taguchi method and crossover operation are inserted between the recognition phase and evolutionary phase for the recombination and diversification of several antibodies to improve the searching ability. Finally, the HTCMIABC algorithm is examined by parameter identification of the nonlinear chaotic system. Simulation results show that the proposed algorithm is more efficient than some typical existing algorithms. The effects of noise and population size are investigated as well. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Chaos operation Chaotic systems Immune algorithm Parameter identification Taguchi method

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Parameter Tuning of artificial bee colony algorithm for Gaussian Noise Elimination on Digital Images

Parameter Tuning of Artificial Bee Colony Algorithm for Gaus...

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International Symposium on Innovations in Intelligent Systems and Applications

作者： Serdar Kockanat Nurhan Karaboga Electronic Communication Technology Sivas Vocational School Cumhuriyet University Sivas Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Erciyes University Kayseri Turkey

ISBN: (纸本)9781479906598

In this paper, the control parameters of the artificial bee colony algorithm were examined to determine for the best performance of the noise elimination problem on gray level digital images. In order to eliminate a noise, a two dimensional finite impulse response digital filter was designed and the artificial bee colony algorithm was used to adjust its coefficient matrix. For the best selected control parameters, the designed two dimensional, finite impulse response digital filter was used to eliminate the Gaussian noise on the gray level digital images at different noise densities and the performance of the designed filter was compared in terms of the noise tolerance.

关键词： artificial bee colony algorithm gaussian noise noise elimination two dimensional digital filter

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