A comparative study of the Bees Algorithm as a tool for function optimisation

Pham, Duc Truong

doi:10.1080/23311916.2015.1091540

Cited by 64 publications

(36 citation statements)

References 29 publications

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“…The misclassification rate under five-fold cross validation was selected as the loss function to be minimised. The BA was chosen for its proven ability to find globally optimal solutions in diverse complex optimisation problems, using both local and global search techniques (Pham, Castellani, & Chen, 2015). Table 1 shows the BA parameter values used.…”

Section: Training Of the Pattern Recognition Systemmentioning

confidence: 99%

A control chart pattern recognition system for feedback-control processes

Torre-Gutiérrez¹,

Pham

2019

Expert Systems with Applications

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Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Training Of the Pattern Recognition Systemmentioning

confidence: 99%

A control chart pattern recognition system for feedback-control processes

Torre-Gutiérrez¹,

Pham

2019

Expert Systems with Applications

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“…In other words, the number of worker bees is equal to the number of sources of food around the hive. The beekeeper whose food supply is out of date becomes a predator bee [18].…”

Section: Bee Optimization Algorithmmentioning

confidence: 99%

Optimal design of solar–wind hybrid system-connected to the network with cost-saving approach and improved network reliability index

Eslami¹,

Kamarposhti

2019

SN Appl. Sci.

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In this paper, the optimal design of a grid-connected the hybrid energy system for a sample area in the north Iran is studied. A new innovative cost-based objective function is proposed which is combination of life cycle cost and reliability cost. Also, loss of power supply probability (LPSP) criteria, is considered as constraint for ensuring at the same time certain level of system reliability. Designing process is implemented in such a way that the total cost of the system reaches its minimum. For this purpose, a modified version of Bee algorithm has been proposed to achieve this goal. In order to carry out studies, the actual sample system, whose data has been available, has been studied. The results indicate the good performance of proposed hybrid system to reduce system cost.

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“…4 demonstrate the flowchart for the proposed algorithm in its simplest form to summarizes the main steps of The Bees Algorithm. As fully described in [14][15][16][17][18][19][20][21][22][23][24][25][26], The Bees Algorithm requires a number of parameters to be set namely: number of scout bees ( ), number of sites selected for exploitation out of n visited sites ( ), number of top-rated (elite) sites among the m selected sites ( ), number of bees recruited for the best e sites ( ), number of bees recruited for the other ( -) selected sites ( ), initial size of each patch (a patch is a region in search space that includes a visited site and its neighborhood) and stopping criterion (iteration number). The LQR controller is pre-designed and its parameters and weight matrices are determined by tuned with The Bees algorithm to minimize the objective function which consists of the angle of pendulum step responses and the positioning of cart step responses in time domain.…”

Section: The Bees Algorithm For Lqr Controller Designmentioning

confidence: 99%

“…The idea is to search for the optimal values of the weighting matrices of LQR controller with respect to a determined objective function. The Bees Algorithm and The objective function created according to existed studies [21][22][23][24][25]…”

Section: The Bees Algorithm For Lqr Controller Designmentioning

confidence: 99%

Tuning of LQR controller for an experimental inverted pendulum system based on The Bees Algorithm

Bilgiç¹,

Şen²,

Kalyoncu³

2016

J. vibroeng.

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Stabilizing of an inverted pendulum (IP) system is a main problem for researchers working on control theory. Balancing of an inverted pendulum system is one of the major benchmark problems in the control system community. This paper presents optimal tuning of linear quadratic regulator (LQR) controller with The Bees Algorithm (BA) for a linear inverted pendulum. In this paper, a metaheuristic approach which is a nature-inspired search method that mimics the foraging behavior of honey bees is used for design of LQR controller to obtain optimal performance. In LQR controller design, state (Q) and control (R) weighting matrices are basic parameters of LQR which are tuning by designer using trial and error method in usually. The Bees Algorithm optimizes the weighting matrices of the LQR controller so that it can move the cart to a desired position with the minimum change in pendulum's angle from vertically upright position during the movement. The tuned LQR controller is benchmarked on the linear inverted pendulum experimental device (IP02) that is manufactured by QUANSER Company. After description of the system and The Bees Algorithm, the paper gives the experimental results obtained from the IP02 system to demonstrating the efficiency of the tuning of the LQR controller. Simulation and experimental results are given graphically to show the success of controller. As a result of the paper, the performance of LQR controller shows the effectiveness of The Bees Algorithm which is a diversity method for provide an efficient solution to conventional trial and error design approach.

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A comparative study of the Bees Algorithm as a tool for function optimisation

Cited by 64 publications

References 29 publications

A control chart pattern recognition system for feedback-control processes

A control chart pattern recognition system for feedback-control processes

Optimal design of solar–wind hybrid system-connected to the network with cost-saving approach and improved network reliability index

Tuning of LQR controller for an experimental inverted pendulum system based on The Bees Algorithm

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