Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg
The hunt for MH370
http://bit.ly/1DXRLbu

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Twitter: Registrations are slow!

Publication(s)

A hyper-heuristic approach to sequencing by hybridization of DNA sequences
http://bit.ly/1mlNjL6
A New Model and a Hyper-heuristic Approach for Two-dimensional Shelf Space Allocation
http://bit.ly/1h1JAhT
Competitive travelling salesmen problem: A hyper-heuristic approach
http://bit.ly/KgQhlh
The optimisation of the single surface mount device placement machine in printed circuit board assembly: a survey
http://bit.ly/fRZIdH

Graham Kendall: Details of Requested Publication


Citation

Nseef, S.K; Abdullah, A; Turky, A and Kendall, G An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowledge-Based Systems, 104: 14-23, 2016.


Abstract

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1016/j.knosys.2016.04.005 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2014 (2.947), 2013 (3.058), 2012 (4.104), 2011 (2.422), 2010 (1.574), 2009 (1.308), 2008 (0.924), 2007 (0.574), 2006 (0.576), 2005 (0.696), 2004 (0.645), 2003 (0.842), 2002 (0.384), 2001 (0.252), 2000 (0.307)

URL

This pubication does not have a URL associated with it.

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


Bibtex

@ARTICLE{natk2016, author = {S.K. Nseef and A. Abdullah and A. Turky and G. Kendall},
title = {An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems},
journal = {Knowledge-Based Systems},
year = {2016},
volume = {104},
pages = {14--23},
abstract = {Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.},
doi = {10.1016/j.knosys.2016.04.005},
issn = {0950-7051},
owner = {Graham},
timestamp = {2016.05.05},
webpdf = {http://www.graham-kendall.com/papers/natk2016.pdf} }