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

Does AI have a place in the board room?
http://bit.ly/1DXreuW
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Tracking Paper Downloads: Database

Publication(s)

A novel approach to independent taxi scheduling problem based on stable matching
http://bit.ly/1A3GUfR
Evidence and belief in regulatory decisions Incorporating expected utility into decision modelling
http://bit.ly/1iaJTKT
Searching the Hyper-heuristic Design Space
http://bit.ly/1sD4RoY
Collective Behavior and Kin Selection in Evolutionary IPD
http://bit.ly/if34nF

Graham Kendall: Details of Requested Publication


Citation

Sabar, N.R; Chong, S.Y and Kendall, G A Hybrid Differential Evolution Algorithm - Game Theory for the Berth Allocation Problem. In Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pages 77-87, 2015.


Abstract

The berth allocation problem (BAP) is an important and challenging problem in the maritime transportation industry. BAP can be defined as the problem of assigning a berth position and service time to a given set of vessels while ensuring that all BAP constraints are respected. The goal is to minimize the total waiting time of all vessels. In this paper, we propose a differential evolution (DE) algorithm for the BAP. DE is a nature-inspired meta-heuristic that has been shown to be an effective method to addresses continuous optimization problems. It involves a population of solutions that undergo the process of selection and variation. In DE, the mutation operator is considered the main variation operator responsible for generating new solutions. Several mutation operators have been proposed and they have shown that different operators are more suitable for different problem instances and even different stages in the search process. In this paper, we propose an enhanced DE that utilizes several mutation operators and employs game theory to control the selection of mutation operators during the search process. The BAP benchmark instances that have been used by other researchers are used to assess the performance of the proposed algorithm. Our experimental results reveal that the proposed DE can obtain competitive results with less computational time compared to existing algorithms for all tested problem instances.


pdf

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doi

The doi for this publication is 10.1007/978-3-319-13356-0_7 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{sck2015, author = {N.R. Sabar and S.Y. Chong and G. Kendall},
title = {A Hybrid Differential Evolution Algorithm - Game Theory for the Berth Allocation Problem},
booktitle = {Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems},
year = {2015},
pages = {77--87},
abstract = {The berth allocation problem (BAP) is an important and challenging problem in the maritime transportation industry. BAP can be defined as the problem of assigning a berth position and service time to a given set of vessels while ensuring that all BAP constraints are respected. The goal is to minimize the total waiting time of all vessels. In this paper, we propose a differential evolution (DE) algorithm for the BAP. DE is a nature-inspired meta-heuristic that has been shown to be an effective method to addresses continuous optimization problems. It involves a population of solutions that undergo the process of selection and variation. In DE, the mutation operator is considered the main variation operator responsible for generating new solutions. Several mutation operators have been proposed and they have shown that different operators are more suitable for different problem instances and even different stages in the search process. In this paper, we propose an enhanced DE that utilizes several mutation operators and employs game theory to control the selection of mutation operators during the search process. The BAP benchmark instances that have been used by other researchers are used to assess the performance of the proposed algorithm. Our experimental results reveal that the proposed DE can obtain competitive results with less computational time compared to existing algorithms for all tested problem instances.},
doi = {10.1007/978-3-319-13356-0_7},
issn = {2363-6084},
owner = {Graham},
timestamp = {2016.01.10},
webpdf = {http://www.graham-kendall.com/papers/sck2015.pdf} }