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Optimum design of post-tensioned flat slabs with its columns to ACI 318-11 using population based beetle antenna search algorithm

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COMPUTERS & STRUCTURES 2021年 256卷 106520-106520页

作者： Yousif, S. Saka, M. P. Univ Bahrain Dept Civil Engn Isa Town Bahrain

Optimum design of post-tensioned flat slabs with its columns is complicated by the presence of many nonlinear constraints and the non-homogeneous nature of the material used. This study presents three design optimization algorithms where the design provisions are implemented in accordance with ACI 318-11. Design variables are divided into two groups. The first group of variables refers to post-tension slab including the thickness of slab, number of tendons and their section along each of the two directions, additional reinforcements at the top and bottom face of the slab and the punching shear reinforcements. The second group of variables is related with columns including cross-sectional dimen-sions, number, and diameter of reinforcements. The total cost of the structure is taken as objective func-tion to be minimized which consists of the costs of concrete, reinforcements, formwork, and tendons. The optimization frameworks are based on artificial bee colony (ABC), Particle Swarm Optimization (PSO) and beetle Antenna Search (BAS). Each of the algorithms is enhanced with novel improvements. Two design examples, one small and one real size flat slab buildings are considered to demonstrate the appli-cation of the developed algorithms and compare their performance. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Post-tensioned flat slabs Minimum cost design Structural optimization Metaheuristic algorithms beetle antenna search artificial bee colony algorithm Particle swam optimization

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An NSABC algorithm for multi-aisle AS/RS scheduling optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107254-107254页

作者： Yan, Xiaohui Zhang, Zhicong Liu, Qunfeng Lv, Cixing Zhang, Liangwei Li, Shuai Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

A multi-objective scheduling optimization model for multi-aisle AS/RS is established in this study, considering the sequence constraint of inbound and outbound tasks with a multi-carrier S/R machine. The aisle switching time is redefined according to the most common layout, which is more realistic than in some of the literature. A non-dominated sorting artificial bee colony (NSABC) algorithm is proposed to solve the problem, which combines the multi-objective handling framework of NSGA-II and the neighborhood search of the ABC algorithm. The encoding structure, decoding method, and updating operators of the solutions are redesigned according to the features of the model. In experiments, operators and parameters of the NSABC algorithm are tested and fine-tuned. The best version is then tested on several groups of instances and compared with the EMBBO, NSGA-II, and MOEA/D algorithms. The results show that the proposed NSABC algorithm outperforms the comparison algorithms in most instances, which indicates that it is an effective approach for the AS/RS scheduling optimization problem.

关键词： Scheduling optimization Multi-objective optimization Automated storage and retrieval system Non-dominated sorting artificial bee colony algorithm

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Weight Optimization for LQG Controller Based on the artificial bee colony algorithm

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AASRI Procedia 2012年 3卷 686-693页

作者： Weidong Wang Yijuan Jing Li Yang Boyuan Ma Zhengguo Fu School of Electrical and Mechanical Engineering Xidian University Xi’an 710071 CHINA

In large antenna servo control systems, Linear Quadratic Gaussian (LQG) controller is superior to the proportional integral (PI) controller in terms of stability, rapidity and wind disturbance suppression. However, there is still no deterministic method to design the weight matrixes that affect the performance of LQG controller. To solve this problem, a method based on the artificial bee colony (ABC) algorithm is proposed, which aims at optimizing the close-loop system transient response performance. Global searching and local searching are conducted in each iterative step in the ABC algorithm so that searching efficiency is greatly improved. Applied in a weight matrixes optimization problem for a large antenna servo with LQG controller, the algorithm is satisfactory in simulation result, the control effect is better than that based on the trial and error method.

关键词： artificial bee colony algorithm LQG controller Weight optimization Large antenna servo system

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Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 163-181页

作者： Ghanem, Waheed A. H. M. Jantan, Aman Univ Sains Malaysia Sch Comp Sci George Town Malaysia Univ Aden Fac Engn Aden Yemen Univ Aden Fac Educ Saber Aden Yemen

The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM.

关键词： artificial bee colony algorithm Monarch butterfly optimization algorithm Global optimization problem Computation intelligence

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Spatio-time-frequency joint sparse optimization with transfer learning in motor imagery-based brain-computer interface system

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102702-102702页

作者： Zheng, Minmin Yang, Banghua Gao, Shouwei Meng, Xia Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Putian Univ Sch Mech & Elect Engn Putian Fujian Peoples R China China Natl Clin Res Ctr Neurol Dis Beijing Peoples R China Capital Med Univ Beijing Tiantan Hosp Dept Neurol Beijing Peoples R China

Motor imagery-based brain-computer interface (MI-BCI) is widely considered as the most promising BCI. Nonstationary of EEG data and long BCIs' calibration time are main problems that affect the practicability of MIBCI. In this paper, we propose a new algorithm, i.e. spatio-time-frequency joint sparse optimization algorithm with transfer learning (STFSTL) to achieve satisfactory classification accuracy with small training set. By introducing artificial bee colony (ABC) algorithm and least absolute shrinkage and selection operator (LASSO), the algorithm optimized parameters in spatial domain, time domain and frequency domain simultaneously. The similarity between data was measured by Euclidean distance. Through instanced-based transfer learning, the source data which was most similar to the target data was selected as the auxiliary data to train the target classifier. We evaluated the performance of the proposed algorithm on three data sets, including a private data set and two public data sets. The classification accuracy of the proposed algorithm with one fifth of the training data was higher than that of five other algorithms. Paired t-test analysis revealed that the accuracy of STFSTL and that of five other algorithms were significantly different. The experimental results suggested that the proposed algorithm with less target data can effectively achieve higher classification accuracy than traditional algorithms. It's likely to have a broad application prospect in MI-BCI.

关键词： artificial bee colony algorithm Joint optimization Least absolute shrinkage and selection operator Motor imagery Transfer learning

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Multiple traffic sign detection based on the artificial bee colony method

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EVOLVING SYSTEMS 2018年第3期9卷 255-264页

作者： Banharnsakun, Anan Kasetsart Univ Fac Engn Sriracha Comp Engn Dept Computat Intelligence Res Lab CIRLab Sriracha Campus Chon Buri 20230 Thailand

Traffic signs play an important role in warning drivers by providing information about traffic restrictions, directions, and road quality in order to make sure that every driver is kept safe. Over the past decade, the development of autonomous vehicles has been an active area of research. Therefore, automatic traffic sign detection is a crucial part of the intelligent transportation systems that can be used in autonomous vehicles to detect traffic signs on the road. The optimization methods based on a biologically inspired computation are very powerful in solving optimization problems. In this work, we consider the traffic sign detection task as an optimization problem and propose the artificial bee colony (ABC) method, one of the most popular biologically inspired methods, as an alternative approach for solving it. In other words, we aim to present an algorithm for the automatic detection of multiple traffic signs with a circular shape based on solutions generated by the ABC method without considering the conventional Hough transform principles. Experimental results obtained by our method demonstrate that the proposed approach works well for multiple traffic sign detection and outperforms other existing algorithms.

关键词： Multiple traffic sign detection artificial bee colony algorithm Midpoint circle algorithm Color segmentation Intelligent transportation systems

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Enhancing the effectiveness of green technology in manufacturing industry

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Materials Today: Proceedings 2021年 47卷 4298-4305页

作者： Rahul A. Jibhakate Nilesh W. Nirwan Kishor S. Rambhad Mechanical Engineering Department National Institute of Technology Silchar 788010 India Mechanical Engineering Department G H Raisoni College of Engineering Nagpur 440016 India Mechanical Engineering Department JSPM Narhe Technical Campus Pune 411041 India

Manufacturing is the process of transforming raw materials into a finished product. During this transformation, energy, cutting fluids, sound emission, dust particles, scraps, and other waste items contribute an environmental pollution in industries. This paper discussed about various green technology in order to reduce environmental pollution in industries. Manufacturing must employ productive approaches and innovations to realise the dream of a sustainable society. Sustainable manufacturing aims to produce the same amount of product with fewer inputs and reducing product waste during transition. There are several approaches to proposing the realisation of sustainable development that are typically complicated and focused more on resource components. Several variables in industrial units, such as emissions management, waste prevention, and worker environment variables such as noise and aerosol, have yet to be thoroughly investigated. Opportunities for improvement, particularly in manufacturing units, yet to be identified. A systematic study of manufacturing facilities established a practical approach to mathematically explaining the relationships between different process variables and energy consumption, waste reduction, and the workplace environment. Industrial units are modeled and their corresponding process parameters are optimized using the artificial bee colony algorithm (ABC) to increase the greenness of these operations. This study will be used as a reference for the selection and promotion of best practices in green technology in the manufacturing industry.

关键词： artificial bee colony algorithm Manufacturing industry units Green Technology Sustainable Manufacturing

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Lyapunov Based Neural Network Estimator Designed for Grid-Tied Nine-Level Packed E-Cell Inverter 35

Lyapunov Based Neural Network Estimator Designed for Grid-Ti...

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IEEE Applied Power Electronics Conference and Exposition (APEC)

作者： Babaie, Mohammad Sharifzadeh, Mohammad Mehrasa, Majid Al-Haddad, Kamal Univ Quebec Ecole Technol Super GREPCI Montreal PQ Canada Babol Noshirvani Univ Dept Comp & Elect Engn Babol Iran

ISBN: (纸本)9781728148298

This paper presents a robust controller based on Lyapunov Control Theory (LCT) and artificial Neural Network (ANN) for the grid-tied single-phase nine-level Packed E-Cell (PEC9) inverter. The proposed robust controller is designed using nonlinear model of the grid-tied PEC9 in which a proper negative definite function with a Positive Weighting Factor (PWF) obtained by LCT guarantees the designed controller to provide globally asymptotically stability for the system in unsteady conditions. An online ANN Estimator (ANNE) is also designed to update the PWF which has the most effect on controller performance. ANNE is also trained through an online optimization loop based on artificial bee colony (ABC) algorithm that reduces the training time dramatically and enhances precision of the trained estimator. Some experimental and simulation tests are accomplished by dSpace-1104 hardware and MATLAB/Simulink software to confirm the high accuracy and fast transient response of the proposed LCT-ANNE in the current reference tracking and obtaining low injected current THD for both steady-state and variable conditions of the grid-tied PEC9.

关键词： Robust Controller Strategy Lyapunov Control Theory Nine-Level Packed E-Cell (PEC9) Inverter artificial Neural Network artificial bee colony algorithm Online Training Grid-Connected Multilevel Inverter

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Software Construction Oriented Multi-agent Collaborative Modeling and Simulation

Software Construction Oriented Multi-agent Collaborative Mod...

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4th IEEE International Conference on Edge Computing (IEEE EDGE) / IEEE World Congress on Services (SERVICES)

作者： Zhang, Yan Xie, Rong He, Yangfan Li, Bing Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728182032

Improper collaboration frequency causes low efficiency in software development. To improve collaboration efficiency in software construction, we propose a method of multi-agent collaborative modeling and simulation. First, we propose collective action model and individual action model to describe the whole construction environment. Then, we design agents in our simulation, establish a mapping relationship between artificial bee colonies and developers group, and propose buzz factor as a parameter to describe the collaboration frequency. In particular, we introduce Q-learning algorithm to obtain the dynamic buzz factor that maximizes construction efficiency. Finally, we perform experiments on groups of agents based on our model to observe the effect of buzz factor. Comparing to the classic feature-oriented stigmergy-based model, our experiment results show a better construction efficiency and provide a reference for controlling the collaboration. Through our work, we can find a suitable collaboration frequency through the simulation, to guide the practice of software development.

关键词： software construction collaborative modeling multi-agent simulation artificial bee colony algorithm Q-learning

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An improved approximation for the Nakagami-m inverse CDF using artificial bee colony optimization

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WIRELESS NETWORKS 2018年第2期24卷 663-669页

作者： Kabalci, Yasin Omer Halisdemir Univ Dept Elect & Elect Engn Fac Engn TR-51240 Nigde Turkey

The quantile function [or inverse cumulative distribution function (CDF)] is a probabilistic measure that is widely employed in both statistical applications and Monte Carlo methods. In addition, this function is important to determine performances of the communication systems, especially for wireless communication systems. However, numerical computing of the Nakagami-m inverse CDF is quite difficult because of the fact that a closed-form expression of the Nakagami-m inverse CDF is not available. In this paper, an improved expression for the Nakagami-m inverse CDF is presented by using curve-fitting methods. Furthermore, parameters of the proposed mathematical model are optimized by the help of artificial bee colony algorithm that is a population based meta-heuristic optimization method motivated by the foraging behavior of honey bee swarms. The results acquired by the proposed approximation are also compared with other existing approaches in the literature in terms of complexity and performance. It is shown that the presented approximation is more accurate, simple and effective against to the previously reported approximations.

关键词： Inverse CDF Nakagami-m fading Curve-fitting artificial bee colony algorithm

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