Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition

Pham, Duc Truong; Otri, Sameh; Ghanbarzadeh, Afshin; Koç, Ebubekir

doi:10.1109/ictta.2006.1684627

Cited by 71 publications

(70 citation statements)

References 5 publications

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“…Solomon (1987) The time windows constraint that should not be violated is presented as follow: BA firstly proposed by Pham et al (2005). BA has been applied in various optimisation fields (Pham et al 2006(Pham et al , 2006a(Pham et al , 2006b(Pham et al , 2007Pham & Castellani, 2009). BA consists of two groups of bees, i.e.…”

Section: Vehicle Routing Problems With Time Windows (Vrptw)mentioning

confidence: 99%

Bee Algorithm for the vehicle routing problems with time windows

Alzaqebah¹,

Abdullah²,

Jawarneh³

2016

Fifth International Conference on Advances in Computing, Control and Networking - ACCN 2016

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Abstract-The natural honeybee's behavior has been modelled by researchers to solve optimization problems. This paper introduces the Bee algorithm based on honeybee behavior for the vehicle routing problem with time windows (VRPTW). The algorithm has been tested on Solomon instances. The encoding of the problem and the operations needed to implement algorithm are outlined. Experiments show that on the considered instances, using BA performs almost as well as planning for long-term, while using much less computation time. Overall, computational investigations show that the proposed algorithm is good and promising approach for the VRPTW.Keywords-Foraging behaviour; bee algorithm; vehicle routing problem with time windows.

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Section: Vehicle Routing Problems With Time Windows (Vrptw)mentioning

confidence: 99%

Bee Algorithm for the vehicle routing problems with time windows

Alzaqebah¹,

Abdullah²,

Jawarneh³

2016

Fifth International Conference on Advances in Computing, Control and Networking - ACCN 2016

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“…Despite its relatively short history, the approaches inspired from the behaviours of honey bees have been applied to job shop scheduling (Chong et al 2006), transportation problems (Lucic 2002), partitioning and scheduling problems (Koudil et al 2007), training of multi-layered perception networks (Pham et al 2006a), recognizing patterns in control charts (Pham et al 2006b), optimization of continuous functions (Karaboğa and Baştürk 2007;Pham et al 2006c), water resources management problems (Bozorg and Afshar 2004), data mining problems (Fathian et al 2007;Benatchba et al 2005) and generalized assignment problems (Baykasoğlu et al 2007). Up to date, this algorithm has not been utilized to solve CL problems.…”

Section: Computational Algorithmmentioning

confidence: 99%

“…e utilization of the proposed models in this eld and a number of algorithms based on the behaviours of honey bee colonies have been designed. ese algorithms, namely bee or bee colony algorithms, are mainly inspired from two features -mating (Abbass 2001;Afshar et al 2007;Teo and Abbass 2003;Koudil et al 2007) and food foraging (Lucic 2002;Yang 2005;Pham et al 2006aPham et al , 2006bPham et al , 2006c.…”

Section: Computational Algorithmmentioning

confidence: 99%

Development of a Decision Support System for Solving Container Loading Problems

Dereli

Daş

2010

Transport

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Abstract. e globalization of supply chains and rising fuel costs are forcing container carriers both to minimize the number of trips and to maximize available container space. is makes container loading (CL) a critical process especially in real-life applications. Container loading (CL), which is a di cult problem to be solved, has many applications in container transportation and distribution industries. is article presents a container loading support system (CLSS). e proposed CLSS composes of three main components, including a hybrid Bees Algorithm as the main computational algorithm, the graphical user interface (GUI) and a simulation program. e aim of the designed system is to make the packing pattern more visible to the user in order to simplify the loading process. An illustrative examplea CL problem from literature -is also provided to introduce the operation of the system and to prove its e ciency.

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“…BA have also been applied to many combinatorial optimization problems, ranging from manufacturing [65] to computer image analysis [26], and it is proved to have a very robust performance and a high success rate compared with other intelligent optimisation methods. BA can be used to study the collaboration issues between partners of a supply chain and especially on the study rules a SME should play in a supply chain.…”

Section: ) Bees Algorithm (Ba)mentioning

confidence: 99%

“…Though there have been considerable amount of study of SCM [10][11][12][13][14][15][16], as far as the authors are aware, there have been very limited publications of applying the findings from the biological system study into SCM. However, there is a large amount of biological study by biologists [17][18][19][20][21][22] and a number of natural-inspired algorithms have also been developed [23][24][25][26][27][28][29][30]. These research results and achievements can be analysed and amalgamated for obtaining bio-inspirations for SCM.…”

Section: Introductionmentioning

confidence: 99%

Biological System Behaviours and Natural-Inspired Methods and their Applications to Supply Chain Management

Fan

Zhang

Hapeshi

et al. 2013

AMM

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People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and naturalinspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bioinspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry.

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Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition

Cited by 71 publications

References 5 publications

Bee Algorithm for the vehicle routing problems with time windows

Bee Algorithm for the vehicle routing problems with time windows

Development of a Decision Support System for Solving Container Loading Problems

Biological System Behaviours and Natural-Inspired Methods and their Applications to Supply Chain Management

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