The Bees Algorithm — A Novel Tool for Complex Optimisation Problems

Pham, Duc Truong; Ghanbarzadeh, Afshin; Koç, Ebubekir; Otri, Sameh; Rahim, Siti Rafidah Abdul; Zaidi, Mohd

doi:10.1016/b978-008045157-2/50081-x

Cited by 945 publications

(631 citation statements)

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“…These data were taken from (Pham, 2006). In Table 3 there are shown results of applying our proposed algorithm to the benchmarking functions as well as results of other commonly used stochastic optimisation algorithms which were previously published in (Pham, 2006). Table 3.…”

Section: Results Of Experimentsmentioning

confidence: 99%

“…More specifically, having developed a model of a bee hive that can work as a kind of search engine (Navrat, 2008), we propose to investigate a bee hive as a possibly useful metaphor for optimisation. We took an inspiration from a popular model described in (Pham, 2006). It has been successfully applied to optimisation of mathematical functions.…”

Section: Optimisation Based On a Social Behaviour Of Honey Beesmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Bee Hive Metaphor as a Model for Problem Solving: Search, Optimisation, and More

Návrat¹,

Ezzeddine²,

Jastrzembska³

et al. 2010

Web Intelligence and Intelligent Agents

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Section: Results Of Experimentsmentioning

confidence: 99%

Section: Optimisation Based On a Social Behaviour Of Honey Beesmentioning

confidence: 99%

Exploring the Bee Hive Metaphor as a Model for Problem Solving: Search, Optimisation, and More

Návrat¹,

Ezzeddine²,

Jastrzembska³

et al. 2010

Web Intelligence and Intelligent Agents

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“…The Bees Algorithm is a swarm intelligent method to solve optimization problems and was developed in Pham et al(2006. This optimization algorithm is based on bee colony food foraging behavior in nature.…”

Section: Modified Multi-objective Bees Algorithmmentioning

confidence: 99%

Structure Comparison and Optimal Design of 6-RUS Parallel Manipulator Based on Kinematic and Dynamic Performances

Mirshekari

Ghanbarzadeh

Shirazi

2016

Lat. Am. j. solids struct.

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The effects of distinctive parameters such as revolute joint angle or spherical joint location of mobile platform in a 6-DOF 6-RUS parallel manipulators on workspace, kinematic, and dynamic indices are investigated in this study to select proper structure commensurate with performance. Intelligent multi-objective optimization method is used to design the manipulator. Considering distinctive parameters, relevant relations for developing inverse kinematic and Jacobin matrix are obtained. In order to study dynamic properties, mass matrix is obtained from calculating the total kinetic energy of the manipulator. After modifying multi-objective Bees algorithm, it used to optimize the manipulator structure considering all geometrical parameters with proper constraints. In addition of comparison of three well known 6-RUS manipulators' types, variation diagram of workspace, local and global dynamics and kinematics performance indices have been drawn with respect to structural parameters variation and limitation of these parameters with proper value are determined. Moreover, considering all dimensional parameters, Pareto front line of multi objective optimization of structure is presented based on dynamic and kinematic performance in pre-determined workspace. Based on the results, a fairly comparison among various types of 6-RUS manipulators can be conducted and the most appropriate set of dimensional parameters are selected based on specific demand. are made of mobile platform which is connected to a fixed platform by several parallel arms. Parallel manipulators can be classified based on degree of freedom, number of arms, order of joints in each arm and type of actuator (Merlet, 2006). according to this, various 6-DOF parallel manipulators have been proposed. One of the most important 6-DOF parallel manipulators is 6-RUS manipulators that revolute joint, universal joint, and spherical joint are used in each arm, respectively.A 6-RUS manipulators has some advantages that the most important one, is low weight of movable parts because of installing motor in the fixed platform. Therefore, bigger and cheaper electrical motors can be used. In addition, thinner connecting rod can be used to the mobile platform that conclude reduction of collision of links to each other (Bonev, 2002). Moreover, these manipulators can be balanced statically (Gosselin and Wang, 2000). Although this type of manipulator has some disadvantages such as bending in connecting rods and complicated mechanical analysis. Also, because of great number of chains, connecting the fixed base with the moving platform, and movment limitations of passive joints, such as spherical and universal, workspace of the 6-RUS manipulators are restericted. To overcome such drawbacks, some researchers demonstrate the tendency for the use less than 6-degree-of-freedom parallel mechanisms (Chablat and Wenger, 2003;Gosselin et al., 2007;Clavel, 1991) and for 6-dof applications hybrid manipulators are studied (Caro et al., 2015).The first 6-RUS manipulator was proposed b...

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“…In the last step, the remaining bees in the population are assigned randomly around the search space scouting for new potential solutions. These steps are repeated until a stopping criterion is met (Pham et al, 2006a).…”

Section: Literature Reviewmentioning

confidence: 99%

“…BA algorithm is a population-based and biological inspired algorithm that has been proposed to overcome the local optima problem, used for both combinatorial and functional optimisation problems (Pham et al, 2006a). BA is used to solve many problems like: control chart pattern recognition, job scheduling, data clustering, function optimisation, etc (Shatnawi et al, 2013b).…”

Section: Literature Reviewmentioning

confidence: 99%

Data Clustering Using Levy Flight and Local Memory Bees Algorithm

Shatnawi

2017

IJBIDM

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Abstract:One of the most popular clustering algorithms is K-means cluster due to its simplicity and efficiency. Although clustering using K-means algorithm is fast and produces good results, it still has a number of limitations including initial centroid selection and local optima. The purpose of this research is to develop a hybrid algorithm that address k-means clustering limitations and improve its performance by finding optimal cluster centre. In this paper, Lévy-flights or Lévy motion is one of non-Gaussian random processes used to solve the initial centroid problem. Bees algorithm is a population-based algorithm which has been proposed to overcome the local optima problem, used along with its local memory to enhance the efficiency of K-means. The proposed algorithm applied to different datasets and compared with K-means and basic Bess algorithm. The results show that the proposed algorithm gives better performance and avoid local optima problem.Keywords: clustering; k-means; Lévy-flights; bees algorithm.Reference to this paper should be made as follows: Shatnawi, N.M. (2017) 'Data clustering using Lévy flight and local memory bees algorithm', Int.

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The Bees Algorithm — A Novel Tool for Complex Optimisation Problems

Cited by 945 publications

References 1 publication

Exploring the Bee Hive Metaphor as a Model for Problem Solving: Search, Optimisation, and More

Exploring the Bee Hive Metaphor as a Model for Problem Solving: Search, Optimisation, and More

Structure Comparison and Optimal Design of 6-RUS Parallel Manipulator Based on Kinematic and Dynamic Performances

Data Clustering Using Levy Flight and Local Memory Bees Algorithm

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