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.

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Publication(s)

A scheme for determining vehicle routes based on Arc-based service network design
http://bit.ly/2iaUTxA
Chapter 2: Iterated prisoner's dilemma and evolutionary game theory
http://bit.ly/1dhTcRf
Chapter 2: Iterated prisoner's dilemma and evolutionary game theory
http://bit.ly/1dhTcRf
A local search approach to a circle cutting problem arising in the motor cycle industry
http://bit.ly/dJxzGW

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.


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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} }