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A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction

ENERGIES 2017年第4期10卷

作者： Hu, Mengyue Hu, Zhijian Yue, Jingpeng Zhang, Menglin Hu, Meiyu Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China

Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD);second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.

关键词： wind power prediction prediction intervals variational mode decomposition kernel extreme learning machine artificial bee colony algorithm

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Multilevel Image Thresholding Selection Using the artificial bee colony algorithm

Multilevel Image Thresholding Selection Using the Artificial...

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International Conference on artificial Intelligence and Computational Intelligence (AICI)

作者： Horng, Ming-Huwi Jiang, Ting-Wei Natl Pingtung Inst Commerce Dept Comp Sci & Informat Engn Pingtung Taiwan

ISBN: (纸本)9783642165269

Image thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied. A new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed in this paper called the maximum entropy based artificial bee colony thresholding (MEABCT) method. Three different methods, such as the methods of particle swarm optimization, HCOCLPSO and honey bee mating optimization are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Meanwhile, the results using the MEABCT algorithm is the best and its computation time is relatively low compared with other four methods.

关键词： Maximum entropy thresholding artificial bee colony algorithm particle swarm optimization honey bee mating optimization

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Damage localization in a cable-stayed bridge via bio-inspired metaheuristic tools

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STRUCTURAL CONTROL & HEALTH MONITORING 2017年第5期24卷

作者： Casciati, Sara Elia, Lorenzo Univ Catania Siracusa Dept Civil Engn & Architecture DICAr Piazza Federico di Svevia 1 I-96100 Siracusa Italy Univ Pavia Dept Civil Engn & Architecture DICAr Via Ferrata 3 I-27100 Pavia Italy

Structural health monitoring is conceived to detect abnormal behaviors in structural systems. A highly non-linear objective function built on the discrepancies between true and generated modal features can be minimized for this purpose. After a finite element discretization is built, the design variables are chosen, and the optimization problem solved. Two bio-inspired metaheuristic tools, namely the artificial bee colony and the firefly algorithm, are employed to proceed toward the global minima. Comparing both identified and analytical stiffness matrices, the damage localization is performed. These methods are tested on a cable-stayed bridge placed in northern Italy. The efficiency of these tools is compared. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： artificial bee colony algorithm damage localization cable-stayed bridge firefly algorithm metaheuristic algorithms optimization

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Robust fault detection and isolation scheme using fuzzy wavelet network with a hybrid design algorithm

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SCIENTIA IRANICA 2017年第3期24卷 1467-1481页

作者： Shahriari-kahkeshi, M. Shahrekord Univ Fac Engn POB 115 Shahrekord Iran

This paper presents a new robust fault detection and isolation scheme using fuzzy wavelet network based on the bounded error approach. An efficient hybrid design algorithm, which consists of the orthogonal least square and the artificial bee colony algorithms, is proposed to design fuzzy wavelet network for modeling normal and faulty behaviors of the system. The proposed model provides an alternative description of the behavior of the system with high accuracy, but it suffers from model uncertainty because of model-reality mismatch in practical applications. To overcome this difficulty, the bounded error approach inspired from robust identification theory is applied to estimate the model uncertainty which defines a confidence interval of the model output and derives adaptive threshold for residual evaluation. Also, online fault isolation process is performed using fuzzy wavelet network models of the faulty system and analyzing the relation between a bank of residuals. Performance and efficiency of the proposed scheme is evaluated by simulating the nonlinear two-tank liquid level control system. Finally, some performance indexes are defined, and then the Monte-Carlo analysis is carried out to evaluate the reliability and robustness of the proposed scheme. (C) 2017 Sharif University of Technology. All rights reserved.

关键词： Robust fault detection and isolation Fuzzy wavelet network Adaptive threshold generation Bounded-error approach artificial bee colony algorithm

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Closed-form design equations for asymmetric coplanar strip line with an infinitely wide strip

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ELECTROMAGNETICS 2017年第6期37卷 411-421页

作者： Yildiz, Celal Kisioglu, Hakan Ozturk, Celal Erciyes Univ Dept Elect & Elect Engn Fac Engn Kayseri Turkey Bozok Univ Dept Elect & Elect Engn Fac Engn & Architecture Yozgat Turkey

This article presents very simple closed-form design equations to calculate the geometrical dimensions of asymmetric coplanar strip line with an infinitely wide strip (CPSIWS). The coefficients of the design equations are determined by artificial bee colony algorithm (ABCA) that is a powerful optimization technique. The results of the proposed design equations are compared with the results of other design equations, experimental works previously published in the literature, quasi-static analysis in the literature, and IE3D software. Accuracy of the characteristic impedances calculated by the design equation results proposed in this work is found to be better than 0.53% with respect to the desired characteristic impedances for 1,960 asymmetric CPSIWS samples.

关键词： artificial bee colony algorithm asymmetric coplanar strip line design equations microwave

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Extreme learning machine-based surrogate model for analyzing system reliability of soil slopes

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Revue Européenne de Génie Civil 2017年第11期21卷 1341-1362页

Geotechnical engineering problems are characterised by many sources of uncertainty, and reliability analysis is needed to take the uncertainties into account. An intelligent surrogate model based on extreme learning machine is proposed for slope system reliability analysis. The weights and bias which play an important role in the performance of ELM are optimised by a nature inspired artificial bee colony algorithm. The system failure probability of soil slopes is estimated by Monte Carlo simulation via the proposed surrogate model. Experimental results show that the proposed method is feasible, effective and simple to implement system reliability analysis of soil slopes.

关键词： slope reliability analysis extreme learning machine artificial bee colony algorithm surrogate model

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Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system

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ENERGY CONVERSION AND MANAGEMENT 2016年 128卷 178-190页

作者： Singh, Shakti Singh, Mukesh Kaushik, Subhash Chandra Thapar Univ Dept Elect & Instrumentat Engn Patiala Punjab India Indian Inst Technol Ctr Energy Studies Delhi India

Renewable energy systems are proving to be promising and environment friendly sources of electricity generation, particularly, in countries with inadequate fossil fuel resources. In recent years, wind, solar photovoltaic (PV) and biomass based systems have been drawing more attention to provide electricity to isolated or energy deficient regions. This paper presents a hybrid PV-wind generation system along with biomass and storage to fulfill the electrical load demand of a small area. For optimal sizing of components, a recently introduced swarm based artificial bee colony (ABC) algorithm is applied. To verify the strength of the proposed technique, the results are compared with the results obtained from the standard software tool, hybrid optimization model for electric renewable (HOMER) and particle swarm optimization (PSO) algorithm. It has been verified from the results that the ABC algorithm has good convergence property and ability to provide good quality results. Further, for critical case such as the failure of any source, the behavior of the proposed system has been observed. It is evident from the results that the proposed scheme is able to manage a smooth power flow with the same optimal configuration. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： artificial bee colony algorithm Battery energy storage system Biomass energy Optimization Solar energy Wind energy

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Swarm intelligence approaches for multidepot salesmen problems with load balancing

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APPLIED INTELLIGENCE 2016年第4期44卷 849-861页

作者： Pandiri, Venkatesh Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Given a set of n cities and m salesmen stationed at d depots, the fixed destination multidepot salesmen problem consists in finding tours for all the salesmen which start and end at their corresponding fixed depots such that each city is visited exactly once by one salesman only, the workload among salesmen is balanced and the total distance traveled by all the salesmen is minimized. In this paper, we have proposed two swarm intelligence approaches for this problem. The first approach is based on artificial bee colony algorithm, whereas the latter approach is based on invasive weed optimization algorithm. Computational results on instances derived from TSPLIB show the effectiveness of our proposed approaches over other state-of-the-art approaches for this problem.

关键词： artificial bee colony algorithm Constrained optimization Invasive weed optimization algorithm Multidepot salesmen problem Swarm intelligence

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USAGE OF artificial NEURAL NETWORK FOR ESTIMATING OF THE ELECTROSPUN NANOFIBER DIAMETER

USAGE OF ARTIFICIAL NEURAL NETWORK FOR ESTIMATING OF THE ELE...

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2017 International artificial Intelligence and Data Processing Symposium (IDAP)

作者： Yilmaz, Cagdas Ustun, Deniz Akdagli, Ali Mersin Univ Fac Engn Dept Elect Elect Engn Mersin Turkey Mersin Univ Fac Technol Dept Software Engn Mersin Turkey

ISBN: (纸本)9781538618806

At the present time, nanomaterials are used in the medicine and biology applications such as drug and gene delivery, bio-detection of pathogens, MRI contrast enhancement, tumor destruction via heating, and protein detection. Tissue engineering which is another of these applications is being increasingly popular. Because extracellular matrix (ECM) consists nano-sized structure;the usage of nanomaterials in tissue engineering enables to produce of tissue scaffolds that are more closely resemble the ECM form. Thus, the success rate increases in tissue engineering as it is provided a more favorable environment for the growth of cells. Electrospinning is a popular method among nanomaterial production ones. The diameter of the fiber produced by electrospinning technique depends on the various parameters like process, solution, and environmental parameters. In this study, an ANN model based on multilayer perceptron (MLP) is presented for predicting the average fiber diameter (AFD) of electrospun gelatin/bioactive glass (Gt/BG) scaffold. The experimental results previously published in the literature, which include one solution parameter (BG content) as well as two process parameters (tip to collector distance and solution flow rate) related to producing of electrospun Gt/BG nanofiber, have been used. The values of average percentage error between the predicted average fiber diameters and experimental ones are achieved as 3.27 %. The results obtained from the proposed model have also been confirmed by comparing with results of AFD expression reported elsewhere. It is illustrated that the AFD of electrospun Gt/BG can be accurately predicted by the model proposed here without requiring any complicated or sophisticated knowledge of the mathematical and physical background.

关键词： Electrospinning Tissue scaffold Nanofibers Modeling artificial bee colony algorithm

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Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence

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APPLIED MATHEMATICAL MODELLING 2016年第11-12期40卷 6105-6120页

作者： Kang, Fei Xu, Qing Li, Junjie Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Surrogate model methods are attractive ways to improve the efficiency of Monte Carlo simulation (MCS) for structural reliability analysis. An intelligent surrogate model based method for slope system reliability analysis is presented in this study. The novel machine learning technique nu-support vector machine (nu-SVM) is adopted to establish the surrogate model to predict the factor of safety via the samples generated by Latin hypercube sampling. Global optimization algorithms particle swarm optimization and artificial bee colony algorithm are adopted to select the hyper-parameters of nu-SVM model. The applicability of the nu-SVM based surrogate model for slope system reliability analysis is tested on four examples with obvious system effects. It is found that the proposed surrogate model combined with MCS can achieve accurate system failure probability evaluation using fewer deterministic slope stability analyzes than other approaches. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Slope reliability Support vector regression Spencer's method Probabilistic analysis artificial bee colony algorithm

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