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Modeling Design Iteration in Product Design and Development and Its Solution by a Novel Artificial Bee Colony algorithm

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2014年第1期2014卷 240828-240828页

作者： Chen, Tinggui Xiao, Renbin Zhejiang Gongshang Univ Coll Comp Sci & Informat Engn Hangzhou 310018 Zhejiang Peoples R China Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Hubei Peoples R China

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

关键词： ITERATIVE methods (Mathematics) PRODUCT design NEW product development bees algorithm COMPETITION (Economics) QUALITY of products

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Detection of electrocardiogram signals using an efficient method

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APPLIED SOFT COMPUTING 2014年 22卷 108-117页

作者： Ebrahimzadeh, A. Shakiba, B. Khazaee, A. Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran

Automatic detection of electrocardiogram (ECG) signals is very important for clinical diagnosis of heart disease. This paper investigates the design of a three-step system for recognition of the five types of ECG beat. In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals. Feature extraction module extracts higher order statistics of ECG signals in combination with three timing interval features. Then hybrid bees algorithm-radial basis function (RBF_BA) technique is used to classify the five types of electrocardiogram (ECG) beat. The suggested method can accurately classify and discriminate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). Finally, the classification capability of five different classes of ECG signals is attained over eight files from the MIT/BIH arrhythmia database. Simulation results show that classification accuracy of 95.79% for the first dataset (4000 beats) and an overall accuracy of detection of 95.18% are achieved over eight files from the MIT/BIH arrhythmia database. (C) 2014 Elsevier B.V. All rights reserved.

关键词： ECG beat classification Higher order statistics Radial basis function neural network bees algorithm

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A new objective function for adaptive distance and directional over-current relays coordination

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第9期28卷 1-1页

作者： Moravej, Zahra Ardebili, Hemad Mohaghegh Semnan Univ Fac Elect & Comp Engn Semnan Iran

The complexity and importance of relay coordination problem are added by increasing interconnection and interpenetration of power networks. Online, adaptive, fast, and reliable re-coordination of relays are more needed to provide requirements of both clients and the new smart and fast-changing power network. In this paper, distance and directional over-current relays coordination problem is solved adaptively by using modified bees algorithm and changing the conventional objective function by using flexible coordination time intervals and pickup currents. The modification on the optimization algorithm has shown up to 50% improvement in efficiency that can be used in other optimization problems. 8, 14, and 30 bus sample networks were simulated to evaluate proposed method. The result of the optimization problem has shown a very good improvement compared with the methods that have been used in prior researches.

关键词： bees algorithm directional over-current relay distance relay optimal relay coordination optimization power system protection

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Quality of service in manufacturing networks: a service framework and its implementation

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期63卷 1227-1237页

作者： Xu, Wenjun Zhou, Zude Pham, D. T. Liu, Quan Ji, C. Meng, Wei Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Univ Birmingham Sch Mech Engn Birmingham W Midlands England Cardiff Univ Sch Engn Cardiff S Glam Wales

Due to the continuous growth in the application of networks in manufacturing, quality of service (QoS) has become an important issue. In this paper, the concept of QoS for manufacturing networks is discussed. To provide overall performance assurance for manufacturing networks, a service framework integrating the QoS mechanisms of the networked resource service management function and the communication networks is proposed. The novel framework maps an application to resource services and then to communication networks, adopts an intelligent optimisation algorithm for QoS management of resource services, and provides QoS schemes for data transfer across communication networks. A prototype implementation has been realised and a set of simulation experiments conducted to evaluate the validity of the framework. The results obtained demonstrate the ability of the framework to satisfy the various performance requirements posed by such applications and provide efficient overall performance assurance for manufacturing networks.

关键词： Manufacturing networks Quality of service Networked resource service management bees algorithm Communication networks

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A novel unsupervised bee colony optimization (UBCO) method for remote-sensing image classification: a case study in a heterogeneous marsh area

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2016年第24期37卷 5726-5748页

作者： Li, Huapeng Zhang, Shuqing Ding, Xiaohui Zhang, Ce Cropp, Roger Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Lancaster Lancaster Environm Ctr Lancaster England Griffith Univ Griffith Sch Environm Gold Coast Australia

Unsupervised image classification is an important means to obtain land-use/cover information in the field of remote sensing, since it does not require initial knowledge (training samples) for classification. Traditional methods such as k-means and Iterative self-organizing data analysis technique (ISODATA) have limitations in solving this NP-hard unsupervised classification problem, mainly due to their strict assumptions about the data distribution. The bee colony optimization (BCO) is a new type of swarm intelligence, based upon which a simple and novel unsupervised bee colony optimization (UBCO) method is proposed for remote-sensing image classification. UBCO possesses powerful exploitation and exploration capacities that are carried out by employed bees, onlookers, and scouts. This allows the promising regions to be globally searched quickly and thoroughly, without becoming trapped on local optima. In addition, it has no restrictions on data distribution, and thus is especially suitable for handling complex remote-sensing data. We tested the method on the Zhalong National Nature Reserve (ZNNR) - a typical inland wetland ecosystem in China, whose landscape is heterogeneous. The preliminary results showed that UBCO (overall accuracy = 80.81%) achieved statistically significant better classification result (McNemar test) in comparison with traditional k-means (63.11%) and other intelligent clustering methods built on genetic algorithm (unsupervised genetic algorithm (UGA), 71.49%), differential evolution (unsupervised differential evolution (UDE), 77.57%), and particle swarm optimization (unsupervised particle swarm optimization (UPSO), 69.86%). The robustness and superiority of UBCO were also demonstrated from the two other study sites next to the ZNNR with distinct landscapes (urban and natural landscapes). Enabling one to consistently find the optimal or nearly optimal global solution in image clustering, the UBCO is thus suggested as a robust method for unsuperv

关键词： REMOTE-sensing images bees algorithm MATHEMATICAL optimization DATA distribution SWARM intelligence

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Developing a Multimodal System for Bee Object Detection and Health Assessment

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IEEE ACCESS 2024年 12卷 158703-158713页

作者： Liang, Andrew Harker Sch San Jose CA 95124 USA

Honey bees pollinate about one-third of the world's food supply, but bee colonies have alarmingly declined by nearly 40% over the past decade due to several factors, including exposure to pesticides. Traditional methods for monitoring beehives, such as human inspections, are subjective, disruptive, and time-consuming. To address these limitations, artificial intelligence has been used to assess beehive health. However, previous studies have lacked an end-to-end solution and primarily relied on data from a single source, either bee images or sounds. This study introduces a unified system for bee object detection, which selectively processes data containing bees for health evaluation. Additionally, it utilizes a combination of visual and audio signals to analyze bee behaviors. A Cross-Attention-based Multimodal Neural Network (CAMNN) was developed to adaptively focus on key features from each type of signal for more accurate bee health assessment. The CAMNN achieved an overall accuracy of 93%, surpassing eight existing single-signal Convolutional Neural Networks and Recurrent Neural Networks. It outperformed the best image-based model by 33% and the top audio-based model by 15%. Furthermore, it improved prediction robustness, attaining an F1-score above 90% across all four evaluated health conditions. The study also showed that audio signals were more reliable than images for assessing bee health. By seamlessly integrating CAMNN with both image and sound data in a holistic bee health monitoring system, this approach provided a more efficient and non-invasive solution for the early detection of bee diseases and the preservation of bee colonies.

关键词： Feature extraction Monitoring Visualization Computational modeling Annotations Accuracy Temperature sensors Deep learning Computer vision bees algorithm artificial intelligence computer vision signal processing electronic beehive monitoring multimodal model cross-attention-based mechanism bee health assessment

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A Comparative Study of Improved Artificial Bee Colony algorithms Applied to Multilevel Image Thresholding

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-17页

作者： Charansiriphaisan, Kanjana Chiewchanwattana, Sirapat Sunat, Khamron Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen 40002 Thailand

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu's method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various metaheuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

关键词： bees algorithm SEGMENTATION (Image processing) THRESHOLDING algorithms MATHEMATICAL optimization PARTICLE swarm optimization METAHEURISTIC algorithms

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Crack identification in post-buckled beam-type structures

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SMART STRUCTURES AND SYSTEMS 2015年第5期15卷 1233-1252页

作者： Moradi, Shapour Moghadam, Peyman Jamshidi Shahid Chamran Univ Dept Mech Engn Ahvaz 61357 Iran Dept Mech Engn Mech Engn Mahshahr 63518 Iran

This study investigates the problem of crack detection in post-buckled beam-type structures. The beam under the axial compressive force has a crack, assumed to be open and through the width. The crack, which is modeled by a massless rotational spring, divides the beam into two segments. The crack detection is considered as an optimization problem, and the weighted sum of the squared errors between the measured and computed natural frequencies is minimized by the bees algorithm. To find the natural frequencies, the governing nonlinear equations of motion for the post-buckled state are first derived. The solution of the nonlinear differential equations of the two segments consists of static and dynamic parts. The differential quadrature method along with an arc length strategy is used to solve the static part, while the same method is utilized for the solution of the linearized dynamic part and the extraction of the natural frequencies of the cracked beam. The investigation includes several numerical as well as experimental case studies on the post-buckled simply supported and clamped-clamped beams having open cracks. The results show that several parameters such as the amount of applied compressive force and boundary conditions influences the outcome of the crack detection scheme. The identification results also show that the crack position and depth can be predicted well by the presented method.

关键词： crack detection beam vibration postbuckling differential quadrature method bees algorithm

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A Global Multilevel Thresholding Using Differential Evolution Approach

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-23页

作者： Charansiriphaisan, Kanjana Chiewchanwattana, Sirapat Sunat, Khamron Khon Kaen Univ Dept Comp Sci Fac Sci Khon Kaen 40002 Thailand

Otsu's function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.

关键词： DIFFERENTIAL evolution (Computer science) PROBLEM solving bees algorithm GLOBAL optimization SEARCH algorithms

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Geometrical Optimization of the Overlap in Mixed Adhesive Lap Joints

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JOURNAL OF ADHESION 2013年第12期89卷 948-972页

作者： Bavi, O. Bavi, N. Shishesaz, M. Sharif Univ Technol Inst Nanosci & Nanotechnol Tehran Iran Shahid Chamran Univ Dept Mech Engn Ahvaz Iran

The aim of this research is to optimize the geometry of the overlap in mixed adhesive single- and double-lap joints using a modified version of bees and Genetic algorithms (BA and GA). Accounting for adherends Poisson's ratio in the deduced equilibrium equations, the proposed shear lag model gives a more accurate approximation of joint failure load in comparison with Volkersen's solution. The objective functions used in this work are used separately to maximize the load bearing capacity f and the specific strength (f/w) of the joint. This procedure is applied to optimize aeronautical adhesively bonded assemblies, while taking manufacturing constraints into account. The employed constraints are the application of yield criterion on adherends as well as geometrical constraint on the overlap length. The proposed straightforward procedure provides 18 optimal configurations amid a wide range of changes for optimization variables, among which the designer can take a choice, depending on his/her goal. The efficiency of the two employed algorithms, BA and GA, in searching for the optimum geometrical design of the mixed adhesive joints have also been investigated. The results show the more robust and efficient performance of the modified version of BA over GA in such kinds of engineering problems.

关键词： Aerospace bees algorithm Joint design Lap-Shear Mixed adhesive joints Optimization

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