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Hybrid artificial Intelligence Model Development for Roller-compacted Concrete Compressive Strength Estimation

INTERNATIONAL JOURNAL OF ENGINEERING 2020年第10期33卷 1852-1863页

作者： Ranjbar, A. Barahmand, N. Ghanbari, A. Islamic Azad Univ Larestan Branch Dept Civil Engn Larestan Iran

This study implemented the artificial bee colony (ABC) metaheuristic algorithm to optimize the artificial Neural Network (ANN) values for improving the accuracy of model and evaluate the developed model. Compressive strength of RCC was investigated using mix design materials in three forms, namely volumetric weight input (cement, water, coarse aggregate, fine aggregate, and binder), value ratio (water to cement ratio, water to binder ratio, and coarse aggregate to fine aggregate ratio), as well as the percentage of mix design values of different ages. A comprehensive, proper-range dataset containing 333 mix designs was collected from various papers. The accuracy of the research models was investigated using error indices, namely correlation coefficient, root-mean-square-error (RMSE), mean absolute error (MAE), and developed hybrid models were compared. External validation and Monte Carlo simulation (MCS)-based uncertainty analysis was also used to validate the models and their results were reported. The experimental stage of the prediction of compressive strength values showed significant accuracy of the ANN-ABC model with (MAE=11.49, RMSE=0.920, RME=5.21) compared to other models in this study. Besides, the sensitivity analysis of predictor variables in this study revealed that the variables "specimen age," "binder," and "fine aggregate" were more effective and important in this research. Comparison of the results showed that the improved proposed model using the ABC algorithm was more capable and more accurate in reducing the error rate in providing computational relations compared to the default models examined in the prediction of the compressive strength of RCC and also tried in simplifying computational relations.

关键词： artificial Neural Network artificial bee colony algorithm Roller-compacted Concrete Compressive Strength

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Research on Performance Improvement for STATCOM with LCL Filter Based on ABC-Pareto Multi -Objective Optimization algorithm 22

Research on Performance Improvement for STATCOM with LCL Fil...

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22nd International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Minglei Wang, Xiangyu Qiao, Jinxin Wang, Liguo Harbin Inst Technol Sch Elect Engn & Automat Harin Peoples R China Northeast Petr Univ Sch Elect Engn & Informat Daqing Peoples R China

ISBN: (纸本)9781728133980

The parameters of LCL filter directly affects the reactive power compensation performance of STATCOM. This paper takes the STATCOM with LCL filter as the research object. A Pareto multi-objective optimization algorithm based on artificial bee colony algorithm (ABC-Pareto algorithm) is proposed to choose some suitable LCL filter parameters. The multiple performance indicators of STATCOM are considered, such as the dynamic response speed, the attenuation capability of high-order harmonic current and the loss of fundamental frequency reactive compensation current. Based on these performance indicators simultaneously, ABC-Pareto algorithm is utilized to choose some more suitable LCL filter parameters by iterative calculation. Parameters obtained by optimization algorithm will improve the reactive compensation performance of STATCOM. The results of 20kVar STATCOM simulation model can verify the superiority and effectiveness of the optimized parameters by ABC-Pareto algorithm.

关键词： LCL filter STATCOM artificial bee colony algorithm Pareto multi-objective optimization algorithm.

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OPTIMIZATION OF JUST-IN-TIME PART SUPPLY SCHEDULING IN ASSEMBLY JOB SHOP 16

OPTIMIZATION OF JUST-IN-TIME PART SUPPLY SCHEDULING IN ASSEM...

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16th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Sun, Lin Xiao, Feng Southeast Univ Sch Mech Engn Nanjing 211100 Peoples R China

ISBN: (纸本)9781728142425

Taking the actual assembly job shop as an example, this paper comprehensively considers the resource constraints of the assembly job shop, the capacity of distribution vehicle and the capacity constraints of the station temporary storage area. A mathematical model was constructed with the optimization goal of minimizing the inventory of the temporary storage area and the total transportation energy consumption without allowing the shortage of parts. Based on the idea of chaotic mapping and reverse learning, the food source coding and initialization were redesigned, and a modified discrete artificial bee colony algorithm was constructed to make joint decision on part supply time and delivery quantity. Finally, simulation analysis was performed to verify the feasibility and effectiveness of the algorithm and model.

关键词： Part supply Energy consumption artificial bee colony algorithm Assembly job shop

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A Method of Hybrid Intelligence for UAV Route Planning Based on Membrane System

A Method of Hybrid Intelligence for UAV Route Planning Based...

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Chinese Automation Congress (CAC)

作者： Lai Lei Zou Kun Wu Dewei Han Kun Li Hailin Zheng Qiurong Air Force Engn Univ Sch Informat & Nav Xian Peoples R China

ISBN: (纸本)9781728140940

For current intelligent path optimization method, when the intelligent optimization algorithm searches for the optimal solution, it cannot simultaneously take into account the defects of global and local search capabilities. This paper takes advantage of the parallelism of membranes structure and the advantages of information interaction within the membrane, nested cell membrane structure, firefly algorithm and bee colony algorithm are used as intra-membrane optimization rules, present a hybrid intelligent optimization method based on cell membrane structure, and apply it to route planning optimization. The results show that the proposed algorithm can accurately find the optimal path planning scenarios, and the algorithm can balances local and global search capabilities.

关键词： UAV route planning membrane structure intelligent optimization firefly algorithm artificial bee colony algorithm

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Convergence Estimation of a Structure Identification Method for Discrete Interval Models of Atmospheric Pollution by Nitrogen Dioxide 9

Convergence Estimation of a Structure Identification Method ...

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9th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Darmorost, Iryna Dyvak, Mykola Porplytsya, Nataliya Shynkaryk, Taras Martsenyuk, Yevgeniya Brych, Vasyl Ternopil Natl Econ Univ Dept Comp Sci Ternopol Ukraine Kyiv City Clin Hosp 3 Kiev Ukraine Ternopil Natl Econ Univ Int Tourism & Hotel Bussines Dept Ternopol Ukraine

In this paper, the convergence of the method of structure identification is investigated. Under convergence, we mean finding a single model by expanding interval estimations of parameters in the process of its identif... 详细信息

ISBN: (纸本)9781728104492

关键词： structure identification parameter identification interval estimates of parameters artificial bee colony algorithm interval discrete dynamic model nitrogen dioxide ecological monitoring

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An artificial bee colony algorithm for the minimum routing cost spanning tree problem

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SOFT COMPUTING 2011年第12期15卷 2489-2499页

作者： Singh, Alok Sundar, Shyam Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Given a connected, weighted, and undirected graph, the minimum routing cost spanning tree problem seeks a spanning tree of minimum routing cost on this graph, where routing cost of a spanning tree is defined as the sum of the costs of the paths connecting all possible pairs of distinct vertices in that spanning tree. This problem has several important applications in networks design and computational biology. In this paper, we have proposed an artificial bee colony (ABC) algorithm-based approach for this problem. We have compared our approach against four best methods reported in the literature-two genetic algorithms, a stochastic hill climber and a perturbation-based local search. Computational results show the superiority of our ABC approach over other approaches.

关键词： artificial bee colony algorithm Constrained optimization Heuristic Minimum routing cost spanning tree Swarm intelligence

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Solving reliability redundancy allocation problems using an artificial bee colony algorithm

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COMPUTERS & OPERATIONS RESEARCH 2011年第11期38卷 1465-1473页

作者： Yeh, Wei-Chang Hsieh, Tsung-Jung Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan

This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost. RAP has been an active area of research for the past four decades. The difficulty that one is confronted with the RAP is the maintenance of feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. In this paper nonlinearly mixed-integer reliability design problems are investigated where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. To the best of our knowledge the ABC algorithm can search over promising feasible and infeasible regions to find the feasible optimal/near-optimal solution effectively and efficiently;numerical examples indicate that the proposed approach performs well with the reliability redundant allocation design problems considered in this paper and computational results compare favorably with previously-developed algorithms in the literature. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Reliability design Redundancy allocation problem artificial bee colony algorithm Mixed-integer nonlinear programming

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Risk analysis of dam based on artificial bee colony algorithm with fuzzy c-means clustering

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2011年第5期38卷 483-492页

作者： Li, Haojin Li, Junjie Kang, Fei Dalian Univ Technol Dept Hydraul Engn Fac Infrastruct Engn Dalian Liaoning Prov Peoples R China

During recent years, risk analysis has been introduced into infrastructure engineering, and has greatly improved the design, construction, and operation. In this paper, we study the risk of dams in the perspective of clustering analysis. Fuzzy c-means clustering (FCM) is widely used in many fields since it is simple and fast. However the result of FCM technique is sensitive to the initialization of clustering centres and is easily trapped into local optima. To improve the performance of FCM, an artificial bee colony algorithm (ABC) with FCM is proposed. By introducing ABC, the shortcomings of the original FCM method is overcome. The proposed clustering algorithm is demonstrated on a benchmark classification problem and two dam risk analysis problems. Results show that it is more accurate and robust than FCM, and it is an efficient tool for risk analysis of dams.

关键词： risk analysis dams artificial bee colony algorithm fuzzy c-means

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Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions

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INFORMATION SCIENCES 2011年第16期181卷 3508-3531页

作者： Kang, Fei Li, Junjie Ma, Zhenyue Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China

A Rosenbrock artificial bee colony algorithm (RABC) that combines Rosenbrock's rotational direction method with an artificial bee colony algorithm (ABC) is proposed for accurate numerical optimization. There are two alternative phases of RABC: the exploration phase realized by ABC and the exploitation phase completed by the rotational direction method. The proposed algorithm was tested on a comprehensive set of complex benchmark problems, encompassing a wide range of dimensionality, and it was also compared with several algorithms. Numerical results show that the new algorithm is promising in terms of convergence speed, success rate, and accuracy. The proposed RABC is also capable of keeping up with the direction changes in the problems. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Evolutionary computation artificial bee colony algorithm Rosenbrock method Unconstrained optimization Memetic algorithm

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A novel expression for resonant length obtained by using artificial bee colony algorithm in calculating resonant frequency of C-shaped compact microstrip antennas

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2011年第4期19卷 597-606页

作者： Akdagli, Ali Bicer, Mustafa Berkan Ermis, Seda Mersin Univ Dept Elect & Elect Engn Mersin Turkey Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

This paper presents a novel and simple expression for resonant length to calculate the resonant frequency of C-shaped compact microstrip antennas operating on UHF band applications. C-shaped compact microstrip antennas with different physical dimensions and electrical parameters were simulated by means of a software package that employs the method of finite difference time domain. With the aid of the artificial bee colony algorithm, an expression for the resonant length depending on physical dimensions was constructed by using simulation data. The resonant length expression provided less than 1.6% error on average over the simulated 144 antennas. A comparison between the results obtained in this work and previous results presented in the literature is given to show the accuracy of the proposed expression.

关键词： artificial bee colony algorithm compact microstrip antenna microstrip antennas resonant frequency

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