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
Does AI have a place in the board room?
http://bit.ly/1DXreuW

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Data Visualisation Competition

Publication(s)

Tabu Search Hyper-Heuristic Approach to the Examination Timetabling Problem at University Technology MARA
http://bit.ly/fOXpLD
A scheme for determining vehicle routes based on Arc-based service network design
http://bit.ly/2iaUTxA
Problem Difficulty and Code Growth in Genetic Programming
http://bit.ly/eTibpi
Channel assignment in cellular communication using a great deluge hyper-heuristic
http://bit.ly/gzkrrv

Graham Kendall: Details of Requested Publication


Citation

Sabar, N. R and Kendall, G Aircraft Landing Problem using Hybrid Differential Evolution and Simple Descent Algorithm. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC 2014), pages 520-527, 2014.


Abstract

The aircraft landing problem (ALP) is a practical and challenging optimization problem for the air traffic industry. ALP involves allocating a set of aircrafts to airport runways and allocating landing times for which the goal is to minimize the total cost of landing deviation from the preferred target times. Differential evolution (DE) is a population based algorithm that has been shown to be an effective algorithm for solving continuous optimization problems. However, DE can suffer from slow convergence when utilized for combinatorial optimization problems, thus hindering its ability to return good quality solutions in these domains. To address this we propose a hybrid algorithm that combines differential evolution with a simple descent algorithm. DE is responsible for exploring new regions in the search space, whilst the descent algorithm focuses the search around the area currently being explored. Experimenting with widely used ALP benchmark instances, we demonstrate that the proposed hybrid algorithm performs better than DE without the simple descent algorithm. Furthermore, performance comparisons with other algorithms from the scientific literature demonstrate that our hybrid algorithm performs better, or at least comparably, in terms of both solution quality and computational time.


pdf

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doi

The doi for this publication is 10.1109/CEC.2014.6900251 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{sk2014a, author = {N. R. Sabar and G. Kendall},
title = {Aircraft Landing Problem using Hybrid Differential Evolution and Simple Descent Algorithm},
booktitle = {Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC 2014)},
year = {2014},
pages = {520--527},
abstract = {The aircraft landing problem (ALP) is a practical and challenging optimization problem for the air traffic industry. ALP involves allocating a set of aircrafts to airport runways and allocating landing times for which the goal is to minimize the total cost of landing deviation from the preferred target times. Differential evolution (DE) is a population based algorithm that has been shown to be an effective algorithm for solving continuous optimization problems. However, DE can suffer from slow convergence when utilized for combinatorial optimization problems, thus hindering its ability to return good quality solutions in these domains. To address this we propose a hybrid algorithm that combines differential evolution with a simple descent algorithm. DE is responsible for exploring new regions in the search space, whilst the descent algorithm focuses the search around the area currently being explored. Experimenting with widely used ALP benchmark instances, we demonstrate that the proposed hybrid algorithm performs better than DE without the simple descent algorithm. Furthermore, performance comparisons with other algorithms from the scientific literature demonstrate that our hybrid algorithm performs better, or at least comparably, in terms of both solution quality and computational time.},
doi = {10.1109/CEC.2014.6900251},
owner = {Graham},
timestamp = {2014.08.08},
webpdf = {http://www.graham-kendall.com/papers/sk2014a.pdf} }