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A support vector machine approach for classification of welding defects from ultrasonic signals

NONDESTRUCTIVE TESTING AND EVALUATION 2014年第3期29卷 243-254页

作者： Chen, Yuan Ma, Hong-Wei Zhang, Guang-Ming Xian Univ Sci & Technol Sch Sci Xian 710054 Peoples R China Xian Univ Sci & Technol Sch Mech Engn Xian 710054 Peoples R China Liverpool John Moores Univ Gen Engn Res Inst Liverpool L3 3AF Merseyside England

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

关键词： support vector machine bees algorithm wavelet packet transform defect classification ultrasonic non-destructive evaluation

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A Modified Artificial Bee Colony algorithm Based on Search Space Division and Disruptive Selection Strategy

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-14页

作者： He, Zhen-an Ma, Caiwen Wang, Xianhong Li, Lei Wang, Ying Zhao, Yuan Guo, Huinan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710019 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Peoples Liberat Army Unit 96165 Shangrao 334000 Jiangxi Peoples R China North Optoelect Stock Ltd Xian 710032 Shaanxi Peoples R China Chinese Armed Police Force Engn Univ Xian 710086 Shaanxi Peoples R China

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

关键词： bees algorithm SEARCH algorithms SPACE division multiple access STABILITY theory PERFORMANCE evaluation

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A hybrid controller based on CPG and ZMP for biped locomotion

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2013年第11期27卷 3473-3486页

作者： Massah, Amir B. Zamani, Ali Salehinia, Yaser Aliyari, Mahdi Sh Teshnehlab, Mohammad KN Toosi Univ Technol Dept Elect & Comp Engn Tehran Iran KN Toosi Univ Technol Dept Mech Engn Tehran Iran

Biped locomotion has attracted much attention in recent years. The most successful implemented methods in this area are based on two approaches, central pattern generator (CPG) and zero moment point (ZMP). Unfortunately, neither of these concepts can solely solve the movement challenge completely. In this study, we introduce a hybrid controller to combine the advantages of these methods. The proposed controller is based on two major approaches, CPG and ZMP. This hybrid controller is composed of a trajectory control system and a trajectory generator system. The trajectory control system applied to keep the robot stable uses ZMP as a real time control feedback. The trajectory generator system, which is composed of nonlinear oscillators, generates stable motions. The parameters of CPG are tuned by a new two-stage approach using differential evolution (DE) and bees algorithm (BA). Furthermore, performance of the proposed controller is verified using the robotic simulation software Webots.

关键词： bees algorithm Central pattern generator Differential evolution Webots Zero moment point

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Quaternion-Based Improved Artificial Bee Colony algorithm for Color Remote Sensing Image Edge Detection

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 p1-10页

作者： Liu, Dujin Wang, Huajun Wang, Sen Pu, Guolin Deng, Xiaoya Hou, Xiang Chengdu Univ Technol Coll Geophys Chengdu 610059 Peoples R China Sichuan Univ Arts & Sci Sch Comp Sci Dazhou 635000 Peoples R China China Acad Telecommun Technol State Key Lab Wireless Mobile Commun Beijing 100191 Peoples R China

As the color remote sensing image has the most notable features such as huge amount of data, rich image details, and the containing of too much noise, the edge detection becomes a grave challenge in processing of remote sensing image data. To explore a possible solution to the urgent problem, in this paper, we first introduced the quaternion into the representation of color image. In this way, a color can be represented and analyzed as a single entity. Then a novel artificial bee colony method named improved artificial bee colony which can improve the performance of conventional artificial bee colony was proposed. In this method, in order to balance the exploration and the exploitation, two new search equations were presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Additionally, some more reasonable artificial bee colony parameters were proposed to improve the performance of the artificial bee colony. Then we applied the proposed method to the quaternion vectors to perform the edge detection of color remote sensing image. Experimental results show that our method can get a better edge detection effect than other methods.

关键词： QUATERNIONS bees algorithm REMOTE sensing IMAGE converters EDGE detection (Image processing) COLOR image processing

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Optimization of composite laminates with free edges under extension load and thermal load

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ADVANCED COMPOSITES AND HYBRID MATERIALS 2022年第3期5卷 2206-2222页

作者： Kazemshahi, Behnam Poor, Mohammad Mansuri Saz, Mohammad Shishe Islamic Azad Univ Dept Mech Engn Parand Branch Parand Iran Univ Tehran Dept Mech Engn Tehran Iran Shahid Chamran Univ Ahvaz Dept Mech Engn Ahvaz Iran

In this study, the single-objective and multi-objective optimizations of composite laminates with free-edge boundaries are presented. bees algorithm (BA) is used to search for the optimal design. An analytical technique is utilized to calculate the edge effect stresses at the free boundaries of the laminates. For this purpose, two laminates are considered: a flawless laminate with free straight edges and a laminate with a hole. The two laminates are subjected to uni-axial and thermal loads. The design of optimal layup in single-objective optimization is based on maximizing the interlaminar strength of the laminates. The multi-objective optimization is formulated for maximizing the interlaminar strength, minimizing the weight and the total cost of the composite components. The optimization variables are considered to be the number of layers, stacking sequence, and the thickness of each layer. Moreover, to verify the performance of BA, the results are compared with those of particle swarm optimization (PSO) and those based on resulting from multi-objective optimization of Kursave (KUR) function. The results of this study show the effect of geometric conditions of free edges as well as the type of loading (mechanical and thermal) on the optimization of composite laminates.

关键词： bees algorithm Composite laminate Interlaminar stress Single-objective optimization Multi-objective optimization

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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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Polygonal Approximation Using an Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-10页

作者： Huang, Shu-Chien Natl Pingtung Univ Dept Comp Sci Pingtung City 90003 Taiwan

A polygonal approximation method based on the new artificial bee colony (NABC) algorithm is proposed in this paper. In the present method, a solution is represented by a vector, and the objective function is defined as the integral square error between the given curve and its corresponding polygon. The search process, including the employed bee stage, the onlooker bee stage, and the scout bee stage, has been constructed for this specific problem. Most experiments show that the present method when compared with the DE-based method can obtain superior approximation results with less error norm with respect to the original curves.

关键词： POLYGONS APPROXIMATION theory bees algorithm INTEGRALS VECTOR analysis COMPUTER science

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Fuzzy Multiobjective Optimal Power Flow Based on Modified Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-12页

作者： He, Xuanhu Wang, Wei Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Beijing 100044 Peoples R China

This paper presents a modified artificial bee colony (MABC) algorithm to solve optimal power flow (OPF) problem. In the proposed MABC algorithm, the searching operation for new food source of artificial bee colony (ABC) algorithm is replaced with mutation and crossover operation of differential evolution (DE) algorithm to improve exploitation capacity. The OPF objective functions involve minimization of total fuel cost of generating units, minimization of emission of atmospheric pollutants, minimization of active power losses, and minimization of voltage deviations. The fuzzy satisfaction-maximizing method is utilized to convert the multiobjectives problem into single objective problem. The proposed approach is applied to the OPF problem on IEEE 30-bus test system. And the results are compared with those obtained by other heuristic algorithms, which demonstrate that the MABC algorithm not only has a better exploration capacity but also possesses stronger exploitation capacity and can effectively solve the OPF problem.

关键词： bees algorithm FUZZY systems ARTIFICIAL intelligence PROBLEM solving ELECTRIC power distribution BUSES (Computers)

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Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-23页

作者： Li, Jun-yi Zhao, Yi-ding Li, Jian-hua Liu, Xiao-jun Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200230 Peoples R China Zhongyuan Univ Technol Sch Comp Sci Zhengzhou Peoples R China Jilin Univ Zhuhai Coll Dept Logist & Informat Management Zhuhai Peoples R China

This paper proposes a modified artificial bee colony optimizer (MABC) by combining bee-to-bee communication pattern and multipopulation cooperative mechanism. In the bee-to-bee communication model, with the enhanced information exchange strategy, individuals can share more information from the elites through the Von Neumann topology. With the multipopulation cooperative mechanism, the hierarchical colony with different topologies can be structured, which can maintain diversity of the whole community. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the advantage of the MABC algorithm. Furthermore, we employed the MABC algorithm to resolve the multilevel image segmentation problem. Experimental results of the new method on a variety of images demonstrated the performance superiority of the proposed algorithm.

关键词： bees algorithm COEVOLUTION SEGMENTATION (Image processing) THRESHOLDING algorithms INFORMATION sharing PERFORMANCE evaluation

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Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony algorithm and Local Information Aggregation

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-18页

作者： Li, Linguo Sun, Lijuan Guo, Jian Han, Chong Li, Shujing Nanjing Univ Posts & Telecommun Sch Comp Nanjing 210003 Jiangsu Peoples R China Fuyang Normal Univ Coll Informat Engn Fuyang 236041 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur's entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image's spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposedmethod with Kapur's entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

关键词： bees algorithm MEMBERSHIP functions (Fuzzy logic) THRESHOLDING algorithms SEGMENTATION (Image processing) INFORMATION storage & retrieval systems

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