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Using bees algorithm for material handling equipment planning in manufacturing systems

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第9-12期48卷 1009-1018页

作者： Sayarshad, Hamid Reza Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar *** Iran

In this paper, bees algorithm (BA) has been used for determine the optimal number of material handling equipment (MHE) used on the production centers. The unmet demands become zero at the end of the planning horizon, i.e., the part demands are totally satisfied through the horizon. The newly developed model provides network information, such as unmet demands and number of loaded and empty of MHE at any given time and centers. Consequently, the model provides a tool for helping managers with planning and decision-making in manufacturing systems. Computational tests showed that small-sized instances can be solved by the exact approach in a fair amount of central processing unit time, but it is not feasible for medium and large-sized instances. To tackle this problem, a bees algorithm is proposed to solve the model. The algorithm is a search procedure inspired by the way honeybees forage for food. The results obtained show the robust performance of the bees algorithm.

关键词： bees algorithm Manufacturing systems Material handling equipment

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Design of a Two-dimensional Recursive Filter Using the bees algorithm

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International Journal of Automation and computing 2010年第3期7卷 399-402页

作者： D.T.Pham Ebubekir Koc Manufacturing Engineering Centre Cardiff University Cardiff CF24 3AA UK

This paper presents the first application of the bees algorithm to the optimisation of parameters of a two-dimensional （2D） recursive digital filter. The algorithm employs a search technique inspired by the foraging behaviour of honey bees. The results obtained show clear improvement compared to those produced by the widely adopted genetic algorithm （GA）.

关键词： bees algorithm swarm intelligence optimisation two-dimensional digital filter design.

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Bee-Sensor-C: An Energy-Efficient and Scalable Multipath Routing Protocol for Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2015年第3期11卷 14-14页

作者： Cai, Xuelian Duan, Yulong He, Ying Yang, Jin Li, Changle Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

A wireless sensor network (WSN) is composed of a large collection of sensor nodes with limited resources in terms of battery supplied energy, processing capability, and storage. Therefore, the design of an energy-efficient and scalable routing protocol is a crucial concern for WSN applications. In this paper, we propose Bee-Sensor-C, an energy-aware and scalable multipath routing protocol based on dynamic cluster and foraging behavior of a bee swarm. Bee-Sensor-C is an evolution from beesensor which is a bee-inspired routing protocol for WSNs. First of all, through introducing a dynamic clustering scheme, Bee-Sensor-C offers parallel data transmissions close to the event area. This evolution reduces routing overhead and improves the scalability. Moreover, Bee-Sensor-C adopts an enhanced multipath construction method in order to achieve the balance of the network energy consumption. Besides, Bee-Sensor-C can well support the multicluster scenario. Through simulations, the network performance is evaluated and the results demonstrate that Bee-Sensor-C outperforms the existing protocols in terms of energy efficiency, energy consumption balance, packet delivery rate, and scalability.

关键词： bees algorithm ROUTING protocols (Computer network protocols) WIRELESS sensor networks ENERGY consumption WIRELESS sensor nodes DATA transmission systems

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PCB assembly optimisation using the bees algorithm enhanced with TRIZ operators

PCB assembly optimisation using the Bees Algorithm enhanced ...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Ang, Mei C. Pham, D. T. Soroka, Anthony J. Ng, Kok W. Cardiff Univ Mfg Engn Ctr Cardiff CF24 3AA S Glam Wales

ISBN: (纸本)9781424452262

This paper describes an application of the bees algorithm with new operators inspired by the TRIZ methodology to optimise assembly sequences for a printed-circuit board (PCB) assembly machine. The specific problem investigated is the assembly of PCBs using a machine of the moving-board-with-time-delay type. The results obtained using the bees algorithm with the TRIZ-inspired operators are found to be better than those by other algorithms from the literature including the original bees algorithm.

关键词： PCB assembly optimisation TRIZ operators assembling bees algorithm moving-board-with-time-delay type optimisation printed circuit manufacture printed-circuit board assembly machine

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Training Spiking Neural Models Using Artificial Bee Colony

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015年第1期2015卷 14-14页

作者： Vazquez, Roberto A. Garro, Beatriz A. La Salle Univ Fac Engn Intelligent Syst Grp Mexico City 06140 DF Mexico Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Mexico City 04510 DF Mexico

Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy.

关键词： bees algorithm PATTERN recognition systems NEURON analysis PROBLEM solving NEURAL networks (Computer science) PERFORMANCE evaluation

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Cuckoo Search algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015年第1期2015卷 374873-374873页

作者： Wang, Jie-sheng Li, Shu-xia Song, Jiang-di Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114044 Peoples R China

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

关键词： SEARCH algorithms MATHEMATICAL optimization VELOCITY bees algorithm PARTICLE swarm optimization

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Balancing fuzzy multi-objective two-sided assembly lines via bees algorithm

Balancing fuzzy multi-objective two-sided assembly lines via...

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1st International Symposium on Fuzzy Systems

作者： Ozbakir, Lale Tapkan, Pinar Erciyes Univ Dept Ind Engn Kayseri Turkey

Many real life problems contain imprecise variables, constraints and objectives. Fuzzy set theory gives an opportunity to handle imprecise terms in such situations. Two-sided assembly line balancing (2sALB) problem which is a generalization of the well known simple assembly line balancing problem can also be modeled more realistically by employing fuzzy approaches. Such an approach is presented in this study to model and solve 2sALB problem by employing fuzzy mathematical programming and bees algorithm (BA). 2sALB problem is a combinatorial complex problem. For this reason BA is employed as a search mechanism for obtaining good solutions to it. BA is a relatively new member of swarm intelligence based meta-heuristics that tries to mimic natural behavior of real honey bees in food foraging in solving complex optimization problems. BA is generally applied to continuous optimization in the literature. Its application to combinatorial problems is rare. This study also presents one of the first application of BA to an assembly line balancing problem which is member of combinatorial optimization.

关键词： Fuzzy multi-objective programming two-sided assembly line balancing problem bees algorithm soft computing

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Correction to: Ait Sahed et al., An efficient artificial bee colony algorithm with application to nonlinear predictive control

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International Journal of General Systems 2016年第4期45卷 477页

International Journal of General Systems

关键词： bees algorithm MATHEMATICAL models Predictive control

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Artificial Bee Colony algorithm with Time-Varying Strategy

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2015年第1期2015卷 17-17页

作者： Qin, Quande Cheng, Shi Zhang, Qingyu Li, Li Shi, Yuhui Shenzhen Univ Coll Management Dept Management Sci Shenzhen 518060 Peoples R China Shenzhen Univ Res Inst Business Analyt & Supply Chain Managemen Shenzhen 518060 Peoples R China Univ Nottingham Ningbo China Div Comp Sci Ningbo 315100 Zhejiang Peoples R China Univ Nottingham Ningbo China Int Doctoral Innovat Ctr Ningbo 315100 Zhejiang Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

关键词： bees algorithm TIME-varying systems SWARM intelligence NONLINEAR systems PROBLEM solving

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Modeling Design Iteration in Product Design and Development and Its Solution by a Novel Artificial Bee Colony algorithm

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2014年第1期2014卷 240828-240828页

作者： Chen, Tinggui Xiao, Renbin Zhejiang Gongshang Univ Coll Comp Sci & Informat Engn Hangzhou 310018 Zhejiang Peoples R China Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Hubei Peoples R China

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

关键词： ITERATIVE methods (Mathematics) PRODUCT design NEW product development bees algorithm COMPETITION (Economics) QUALITY of products

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