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Design of phase-only reconfigurable planar array antenna in selected phi cuts using various meta-heuristic optimization algorithms

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2019年第4期44卷 1-8页

作者： Jamunaa, D. Mahanti, G. K. Al Attar, Feras Nadhim Hasoon Natl Inst Technol Dept Elect & Commun Engn Durgapur 713209 India NUST Coll Engn Dept Elect & Comp Engn Muscat Oman

In this paper, a pattern synthesis based on a multiobjective optimization algorithm is proposed for the generation of a reconfigurable pencil/flat top dual-beam planar antenna array built using isotropic antenna elements in selected phi cuts. These beams claim the same amplitude excitations and differ from each other in phase excitations. Zero-phase excitations are used in pencil beam and these phases are updated with optimum phases for the flat top beam. All the excitations are obtained using Moth-flame optimization algorithm. With the support of the fitness functions, care is taken to control the expected values of the radiation pattern parameters to remain under certain fixed limit. In addition, synthesis is also done for the provision of a null in a particular direction for rejection of interference in the pencil beam in two different phi cuts. To suppress the mutual coupling effects, dynamic range ratio is kept under a threshold limit. Simulation results show the effectiveness of this proposed synthesis for phi cut planes. This algorithm is compared and proved to be better in many aspects over the standard meta-heuristic algorithms like artificial bee colony and Imperialist Competitive algorithms in terms of performance parameters.

关键词： Phase-only reconfigurable array antenna Side Lobe Level flat top beam Moth-flame optimization algorithm artificial bee colony algorithm Imperialist Competitive algorithm

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Boosting galactic swarm optimization with ABC

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第9期10卷 2401-2419页

作者： Kaya, Ersin Uymaz, Sait Ali Kocer, Baris Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn Konya Turkey

Galactic swarm optimization (GSO) is a new global metaheuristic optimization algorithm. It manages multiple sub-populations to explore search space efficiently. Then superswarm is recruited from the best-found solutions. Actually, GSO is a framework. In this framework, search method in both sub-population and superswarm can be selected differently. In the original work, particle swarm optimization is used as the search method in both phases. In this work, performance of the state of the art and well known methods are tested under GSO framework. Experiments show that performance of artificial bee colony algorithm under the GSO framework is the best among the other algorithms both under GSO framework and original algorithms.

关键词： Galactic swarm optimization artificial bee colony algorithm Swarm intelligence Metaheuristic optimization algorithm

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The Research on the Motion State Monitoring of Electromagnetic Valve Train of Engine Based on Internet of Things

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IEEE ACCESS 2019年 7卷 89458-89465页

作者： Guo, Tongjun Chang, Siqin Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China

Aiming at real-time monitoring of valve motion online in a new engine valve train that replaces the traditional cam with a self-developed new electromagnetic linear actuator, this paper presents a system scheme for motion monitoring of the electromagnetic linear actuators based on the Internet of Things and support vector machine (SVM) algorithm, which can effectively monitor the running state of the electromagnetic linear actuator and the valve. The system uses the lower computer to collect the current of electromagnetic linear actuator, and real-time transmits the data to the upper computer in the monitoring center. The upper computer firstly extracts the eigenvector of the motion state of the actuator by wavelet packet decomposition method, and then uses the artificial bee colony algorithm (ABC) to optimize the model kernel function parameters and penalty factor C of support vector machine, and establishes ABC-SVM electromagnetic linear actuator's motion state model and training. In this paper, the method is compared with the direct-collected wire-controlled detection method. The results show that the system can accurately and quickly monitor and analyze the electromagnetic linear actuator and engine valve operating state, so it is feasible.

关键词： Electromagnetic linear actuator valve train Internet of Things artificial bee colony algorithm online monitoring and analysis

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Transformer fault diagnosis based on new features selection and artificial bee colony optimization SVM

Transformer fault diagnosis based on new features selection ...

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International Conference on Power System Technology (POWERCON)

作者： Zhang, Yiyi Peng, Hongbo Fang, Jiake Zhao, Liuliang Li, Xin Liao, Changyi Guangxi Key Lab Power Syst Optimizat & Energy Tec Nanning 530004 Peoples R China Guangxi Univ Nanning 530004 Peoples R China

ISBN: (纸本)9781538664612

In order to improve the accuracy of power transformers, a new DGA ratios based on support vector machine (SVM) and quantum-behaved particle swarm optimization with binary encoding (BQPSO) algorithm was proposed in the paper. First of all, 28 DGA ratios are used as the input vectors. Then, the SVM parameters and DGA ratios were simultaneously optimized by BQPSO. Finally, a transformer fault diagnosis model based on the artificial bee colony (ABC) algorithm optimization SVM combined with the cross validation (CV) principle is used to diagnostic fault. The diagnosis results show that the proposed DGA ratios increase the accuracy by 12%-28% over IEC ratios and DGA data. Furthermore, the accuracy of ABCSVM is better than the GASVM and standard SVM (accuracy rate are 84.35%, 79.33%% and 65.41%).

关键词： power transformers fault diagnosis support vector machine BQPSO algorithm artificial bee colony algorithm

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OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning algorithms

OptABC: an Optimal Hyperparameter Tuning Approach for Machin...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Leila Zahedi Farid Ghareh Mohammadi M. Hadi Amini Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA Sustainability Optimization and Learning for InterDependent networks (solid) laboratory FIU Miami FL USA University of Georgia Athens GA USA

ISBN: (纸本)9781665443388

Hyperparameter tuning in machine learning algorithms is a computationally challenging task due to the large-scale nature of the problem. In order to develop an efficient strategy for hyper-parameter tuning, one promising solution is to use swarm intelligence algorithms. artificial bee colony (ABC) optimization lends itself as a promising and efficient optimization algorithm for this purpose. However, in some cases, ABC can suffer from a slow convergence rate or execution time due to the poor initial population of solutions and expensive objective functions. To address these concerns, a novel algorithm, OptABC, is proposed to help ABC algorithm in faster convergence toward a near-optimum solution. OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy for tuning the hyper-parameters of different machine learning models. OptABC employs these techniques in an attempt to diversify the initial population, and hence enhance the convergence ability without significantly decreasing the accuracy. In order to validate the performance of the proposed method, we compare the results with previous state-of-the-art approaches. Experimental results demonstrate the effectiveness of the OptABC compared to existing approaches in the literature.

关键词： Greedy algorithms Machine learning algorithms Sociology Clustering algorithms Machine learning artificial bee colony algorithm Statistics

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A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第6期47卷 1342-1357页

作者： Ma, Chao Ouyang, Jihong Chen, Hui-Ling Ji, Jin-Chao Jilin Univ Coll Comp Sci & Technol Changchun 130023 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130023 Peoples R China Wenzhou Univ Coll Phys & Elect Informat Wenzhou Peoples R China

In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

关键词： extreme learning machine artificial bee colony algorithm self-adaptive learning kernel function

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Modeling the Tennessee Eastman chemical process reactor using bio-inspired feedforward neural network (BI-FF-NN)

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第1-4期103卷 1359-1380页

作者： Sheta, Alaa Braik, Malik Al-Hiary, Heba Texas A&M Univ Dept Comp Sci Corpus Christi TX 78412 USA Al Balqa Appl Univ Dept Comp Sci Salt Jordan Al Balqa Appl Univ Dept Comp Informat Syst Salt Jordan

This study explores the application of bio-inspired algorithms (BIAs) in training artificial neural networks (ANNs) in the area of the nonlinear chemical process modeling. Motivated by the increasing complexity and operational efficiency of chemical processes, the need for schemes that can improve model identification of highly nonlinear systems is demanded. As a case study, the Tennessee Eastman (TE) chemical reactor problem is considered. The key issue is to find a particular architecture for an ANN that best model the TE chemical process. We propose the use of BIAs of bat algorithm (BA), firefly algorithm (FA), and artificial bee colony (ABC) algorithm as mechanisms to automatically update the synaptic weights of ANN. These algorithms were conducted to increase the ability of ANN to adapt the dynamic aspect of the TE process and prevent trapping into a local optimum. The proposed modeling framework was devised with extensive experiments and statistical analysis to illustrate the usability and suitability of the entire modeling and identification procedures. The results were explored with a discussion using the mean square error (MSE) and the variance account for (VAF) criteria to assess the degree of identification performance of the TE case study model. The reliability of the presented models was compared with previously developed models for the same subsystems of the TE reactor using competitive, intelligent approaches. The comparative results show that the proposed approach provides superior modeling performance and outperforms its competitors.

关键词： Tennessee Eastman process Neural networks Bat algorithm Firefly algorithm artificial bee colony algorithm

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Weapon-Target Assignment for Multi-to-Multi Interception With Grouping Constraint

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IEEE ACCESS 2019年 7卷 34838-34849页

作者： Guo, Dong Liang, Zixuan Jiang, Peng Dong, Xiwang Li, Qingdong Ren, Zhang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Energy Internet Res Ctr Changsha 410073 Hunan Peoples R China

This paper presents a weapon-target assignment (WTA) method for a multi-to-multi interception with fixed and adaptive grouping constraints. First, to get a better evaluation of the interception performance, the miss-distance under heading error, the time-to-go, and the line-of-sight rate are used to construct an interception probability function which considers the interception efficiency and the required energy. Second, to provide good engagement conditions for cooperative guidance in the case of multiple missiles against multiple targets, and meanwhile, to ensure that each target is allocated with appropriate interception resources, a fixed grouping strategy and an adaptive grouping strategy based on penalty function are proposed for the WTA problem. Then, the artificial bee colony algorithm is employed to solve the WTA problem with grouping constraints. Finally, two scenarios of multi-to-multi interception are simulated to verify the proposed WTA method. Results indicate that the method can realize the optimal allocation schemes which satisfy both the fixed and the adaptive grouping constraints.

关键词： Weapon-target assignment grouping constraint cooperative attack multi-to-multi interception artificial bee colony algorithm

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A novel method for estimation of intensity and location of multiple point heat sources based on strain measurement

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2019年 98卷 203-216页

作者： Ghiasi, N. Khosravifard, A. Shiraz Univ Dept Mech Engn Shiraz 71936 Iran

A novel approach for estimation of the location and intensity of multiple point heat sources in 2D domains is proposed in this work. The proposed inverse method is based on the simultaneous application of an improved meshless method and artificial intelligence algorithms. The inverse problem is formulated as an optimization problem and is solved by an improved artificial bee colony algorithm. In this work, strain measurements, rather than the usual temperature measurements, are used in the estimation procedure. It is shown that by using the strains for estimation of heat sources, results with better accuracy and more reliability can be obtained. The estimation procedure includes several direct thermo-elasticity problems which are solved by the meshless radial point interpolation method (RPIM) with a modified integration technique. Some numerical results are presented to demonstrate the applicability and efficiency of the proposed method. The results of the proposed approach are compared with those obtained from temperature measurements, and it is shown that especially for materials with high thermal conductivity, the proposed approach is very promising.

关键词： Inverse problem Meshless methods Radial point interpolation method artificial bee colony algorithm Background decomposition method (BDM)

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The color quantization problem solved by swarm-based operations

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APPLIED INTELLIGENCE 2019年第7期49卷 2482-2514页

作者： Perez-Delgado, Maria-Luisa Univ Salamanca Escuela Politecn Super Zamora Ave Requejo 33 Zamora 49022 Spain

The objective of the color quantization problem is to reduce the number of different colors of an image, in order to obtain a new image as similar as possible to the original. This is a complex problem and several solution techniques have been proposed to solve it. Among the most novel solution methods are those that apply swarm-based algorithms. These algorithms define an interesting solution approach, since they have been successfully applied to solve many different problems. This paper presents a color quantization method that combines the artificial bee colony algorithm with the Ant-tree for Color Quantization algorithm, creating an improved version of a previous method that combines artificial bees with the K-means algorithm. Computational results show that the new method significantly reduces computing time compared to the initial method, and generates good quality images. Moreover, this new method generates better images than other well-known color quantization methods such as Wu's method, Neuquant, Octree or the Variance-based method.

关键词： Color quantization artificial ants Ant-tree algorithm artificial bee colony algorithm Clustering

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