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A Novel Artificial Bee Colony algorithm for Function Optimization

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

作者： Zhang, Song Liu, Sanyang Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

It is known that both exploration and exploitation are important in the search equations of ABC algorithms. How to well balance the two abilities in the search process is still a challenging problem in ABC algorithms. In this paper, we propose a novel artificial bee algorithm named as "NABC," by incorporating the information of the global best solution into the solution search equation of the onlookers stage to improve the exploitation. At the same time, we improve the search equation of the employed bees to keep the exploration. The experimental results of NABC tested on a set of 11 numerical benchmark functions show good performance and fast convergence in solving function optimization problems, compared with variant ABC, DE, and PSO algorithms. The application of NABC on solving five standard knapsack problems shows its effectiveness and practicability.

关键词： ARTIFICIAL intelligence bees algorithm NUMERICAL analysis STOCHASTIC convergence PROBLEM solving

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A Simple and Efficient Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-9页

作者： Xu, Yunfeng Fan, Ping Yuan, Ling Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Chinese Peoples Publ Secur Univ Dept Informat Secur Engn Beijing 100038 Peoples R China Hubei Univ Sci & Technol Sch Comp Sci Xianning 437100 Peoples R China

Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

关键词： bees algorithm MATHEMATICAL optimization STOCHASTIC analysis STOCHASTIC convergence PERFORMANCE evaluation FUNCTIONS (Mathematics) SIMULATION methods & models POPULATION research

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Crack detection of plate structures using differential quadrature method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2013年第7期227卷 1495-1504页

作者： Moradi, S. Alimouri, P. Shahid Chamran Univ Dept Mech Engn Ahvaz 61355 Iran

In this study, the problem of crack identification in plates is investigated using the differential quadrature method. The crack, which is assumed to be open, is modeled by the extended rotational spring. The crack, with finite length, divides the plate into six segments. Then, the differential quadrature is applied to the governing differential equations of motion of each segment and the corresponding boundary and continuity conditions. An eigenvalue analysis will be performed on the resulting system of algebraic equations to obtain the natural frequencies of the cracked plate. Here, the crack detection practice is considered as an optimization problem, and the location, size, and depth of the crack are regarded as the design variables. The weighted sum of the squared errors between the measured and computed natural frequencies is used as the objective function. The bees algorithm, a swarm-based evolutionary optimization technique, is used to solve the optimization problem. To insure the integrity and robustness of the presented method, extensive experimental case studies are carried out on the cantilever plates having a finite-length open crack parallel to the clamped edge. The results show that the crack parameters can be predicted well by the presented methodology.

关键词： Differential quadrature crack detection cantilever plate bees algorithm

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Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-17页

作者： Song, Fei Qin, Shiyin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

关键词： bees algorithm FAULT tolerance (Engineering) ROBUST control NATURAL satellites BANDWIDTHS PARAMETERS (Statistics)

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Modeling and solving constrained two-sided assembly line balancing problem via bee algorithms

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APPLIED SOFT COMPUTING 2012年第11期12卷 3343-3355页

作者： Tapkan, Pinar Ozbakir, Lale Baykasoglu, Adil Erciyes Univ Dept Ind Engn Kayseri Turkey Dokuz Eylul Univ Dept Ind Engn Izmir Turkey

Designing and operating two-sided assembly lines are crucial for manufacturing companies which assemble large-sized products such as trucks, buses and industrial refrigerators. This type of assembly line structure has several advantages over one-sided assembly lines such as shortened line length and reduced throughput time. The research area has recently focused on balancing two-sided assembly lines owing to these advantages. However, due to the complex structure of this problem, some practical constraints have been disregarded or have not been fully incorporated. In order to overcome these deficiencies, a fully constrained two-sided assembly line balancing problem is addressed in this research paper. Initially, a mathematical programming model is presented in order to describe the problem formally. Due to the problem complexity, two different swarm intelligence based search algorithms are implemented to solve large-sized instances. bees algorithm and artificial bee colony algorithm have been applied to the fully constrained two-sided assembly line balancing problem so as to minimize the number of workstations and to obtain a balanced line. An extensive computational study has also been performed and the comparative results have been evaluated. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Two-sided assembly line balancing bees algorithm Artificial bee colony algorithm Swarm intelligence Integer programming

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Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms

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SOFT COMPUTING 2014年第5期18卷 871-903页

作者： Pham, D. T. Castellani, M. Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England Univ Bergen Dept Biol N-5020 Bergen Norway

This paper describes an experimental investigation into four nature-inspired population-based continuous optimisation methods: the bees algorithm, Evolutionary algorithms, Particle Swarm Optimisation, and the Artificial Bee Colony algorithm. The aim of the proposed study is to understand and compare the specific capabilities of each optimisation algorithm. For each algorithm, thirty-two configurations covering different combinations of operators and learning parameters were examined. In order to evaluate the optimisation procedures, twenty-five function minimisation benchmarks were designed by the authors. The proposed set of benchmarks includes many diverse fitness landscapes, and constitutes a contribution to the systematic study of optimisation techniques and operators. The experimental results highlight the strengths and weaknesses of the algorithms and configurations tested. The existence and extent of origin and alignment search biases related to the use of different recombination operators are highlighted. The analysis of the results reveals interesting regularities that help to identify some of the crucial issues in the choice and configuration of the search algorithms.

关键词： Optimisation bees algorithm Swarm intelligence Evolutionary algorithms Optimisation benchmarks

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Optimized fuzzy controller for a power-torque distribution in a hybrid vehicle with a parallel configuration

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2014年第14期228卷 1654-1674页

作者： Derakhshan, Mohsen Shirazi, Kourosh Heidari Shahid Chamran Univ Ahvaz Dept Mech Engn Ahvaz Iran

In this study, an optimized fuzzy controller was designed for a power-torque distribution in a hybrid passenger vehicle using the bees algorithm. For this purpose, a dynamic model for a parallel hybrid vehicle, including an internal-combustion engine, an electric motor and battery, a torque converter, a continuously variable transmission, torque-speed coupling and wheels (forward dynamic), is built. To simulate the driver's skill and performance when facing different conditions, a fuzzy controller (which is called the first-layer controller) is used. On the one hand, decisions made by the first-layer controller are a function of the desired acceleration, the desired speed and the speed error and, on the other hand, they depend on the vehicle's performance mode. The vehicle's performance mode is determined with regard to the charged condition (the state of charge) of the batteries, the vehicle's required power and the driver commands by another fuzzy controller which is called the second-layer controller. The necessary commands for the subsystems such as the throttle angle, the controller command for the electric motor and the appropriate speed ratio in the continuously variable transmission are determined by another controller, called the third-layer controller. Both the first layer and the second layer are optimized using the bees algorithm. According to the results, the optimized controller indicates an appropriate performance based on the defined rules in different situations. It also improves the vehicle's performance parameter and decreases the fuel consumption and the pollutions produced by the engine.

关键词： Optimized fuzzy controller torque distribution strategy hybrid electric vehicle parallel configuration bees algorithm

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A cost-sensitive classification algorithm: BEE-Miner

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KNOWLEDGE-BASED SYSTEMS 2016年 95卷 99-113页

作者： Tapkan, Pinar Ozbakir, Lale Kulluk, Sinem Baykasoglu, Adil Erciyes Univ Dept Ind Engn TR-38039 Kayseri Turkey Dokuz Eylul Univ Dept Ind Engn TR-35210 Izmir Turkey

Classification is a data mining technique which is utilized to predict the future by using available data and aims to discover hidden relationships between variables and classes. Since the cost component is crucial in most real life classification problems and most traditional classification methods work for the purpose of correct classification, developing cost-sensitive classifiers which minimize the total misclassification cost remains a subject of much interest. The purpose of this study is to present an effective solution method that configurates and evaluates learning systems from previous experiences, thus aiming to obtain decisions and predictions. Since most real life problems are cost-sensitive and developing effective direct methods for cost-sensitive multi-class classification is still an attractive area, a cost-sensitive classification method, the BEE-Miner algorithm, is proposed by utilizing the recently developed bees algorithm (BA). The main advantages of BEE-Miner are its capability to handle both binary and multi-class problems and to incorporate misclassification cost into the algorithm via generating neighbor solutions and evaluating the quality of the solutions. An extensive computational study is also performed on cost-insensitive and cost-sensitive versions of the proposed BEE-Miner algorithm and effective results on different types of problems are obtained with high test accuracy and low misclassification cost. (C) 2015 Elsevier B.V. All rights reserved.

关键词： bees algorithm Cost-sensitive classification Data mining

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Comparison of CD and CDC Technologies for Flex Grid DWDM Networks

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IEEE ACCESS 2024年 12卷 116026-116039页

作者： Kozdrowski, Stanislaw Krysztofik, Pawel Sujecki, Slawomir Warsaw Univ Technol Comp Sci Inst PL-00665 Warsaw Poland Wroclaw Univ Sci & Technol Telecommun & Teleinformat Dept PL-50370 Wroclaw Poland Mil Univ Technol Fac Elect PL-00908 Warsaw Poland

The main objective of the paper is to compare CD and CDC nodal architecture technologies for flex grid DWDM optical networks. The methodology adopted consists in optimising a DWDM network for both nodal architectures and comparing the resource utilisation. Several nature-inspired heuristic algorithms, based mainly on an evolutionary algorithm, have been specifically selected to solve the optimisation problem with realistic computational resources. Numerical experiments were carried out for two distinct backbone networks: Polish and American. Using the best of the proposed heuristic methods in terms of the value of the objective function and computation time, CD and CDC technologies were compared. The results obtained show the advantages of CDC technology over CD technology, particularly with respect to utilization of occupied frequency bandwidth of network edges.

关键词： Wavelength division multiplexing Optimization Bandwidth Costs Transponders Routing Mathematical models Metaheuristics Evolutionary computation Mixed integer linear programming Optical fiber networks evolutionary algorithm bees algorithm mixed integer linear programming combinatorial optimization new-generation optical network design DWDM CD CDC

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

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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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