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When using a constructive search algorithm, solutions to scheduling problems such as the job shop and open shop scheduling problems are typically represented as permutations of the operations to be scheduled. The combination of this representation and the use of a constructive algorithm introduces a bias typically favouring good solutions. When ant colony optimisation is applied to these problems, a number of alternative pheromone representations are available, each of which interacts with this underlying bias in different ways. This paper explores both the structural aspects of the problem that introduce this underlying bias and the ways two pheromone representations may either lead towards poorer or better solutions over time. Thus it is a synthesis of a number of recent studies in this area that deal with each of these aspects independently.

Item Type:	Refereed Conference Paper
Keywords:	ant colony optimisation, scheduling problems, solution representation, search space, optimisation
Research Division:	Information and Computing Sciences
Research Group:	Artificial Intelligence and Image Processing
Research Field:	Neural, Evolutionary and Fuzzy Computation
Objective Division:	Expanding Knowledge
Objective Group:	Expanding Knowledge
Objective Field:	Expanding Knowledge in the Information and Computing Sciences
UTAS Author:	Montgomery, J (Dr James Montgomery)
ID Code:	97249
Year Published:	2005
Web of Science® Times Cited:	4
Deposited By:	Information and Communication Technology
Deposited On:	2014-12-09
Last Modified:	2016-01-19
Downloads:	3 View Download Statistics