Copyright (c) 2009 Bond University All rights reserved. http://epublications.bond.edu.au/marcus_randall Recent documents in Marcus Randall en-us Sun, 22 Feb 2009 19:30:09 PST 3600 http://epublications.bond.edu.au/infotech_pubs/35 http://epublications.bond.edu.au/infotech_pubs/35 Thu, 07 Jun 2007 23:52:56 PDT The aim of adding explicit intensification / diversification measures to ant colony optimisation is so that it better samples the search space. A new and novel method of achieving this is based on the idea of search space reduction in which solution components are fixed during an intensification stage and certain values for some components are excluded during diversification. These phases are triggered as required throughout the search process. In comparison to an existing intensification / diversification scheme and a control ant colony solver, the results using the travelling salesman problem reveal that the new scheme offers substantial improvement. It often achieves optimal results for benchmark problem instances. Additionally, this scheme requires few extra computational resources and is applicable to a range of combinatorial problems. © Springer-Verlag Berlin Heidelberg 2006. Marcus Randall http://epublications.bond.edu.au/infotech_pubs/34 http://epublications.bond.edu.au/infotech_pubs/34 Thu, 07 Jun 2007 23:23:09 PDT Biological systems are, by their very nature, adaptive. However, the meta-heuristic search algorithms inspired by them have mainly been applied to static problems (i.e., problems that do not change while they are being solved). Recently, a greater body of work has been completed on the newer meta-heuristics, particularly ant colony optimisation, particle swarm optimisation and extremal optimisation. This survey paper examines representative works and methodologies of these techniques on this class of problems. Beyond this we outline the limitations of these methods. Copyright © 2007 IEEE Inc. All rights reserved. Tim Hendtlass http://epublications.bond.edu.au/infotech_pubs/33 http://epublications.bond.edu.au/infotech_pubs/33 Thu, 07 Jun 2007 23:12:39 PDT A relatively new meta-heuristic, known as extremal optimisation (EO), is based on the evolutionary science notion that poorly performing genes of an individual are replaced by random mutation over time. In combinatorial optimisation, the genes correspond to solution components. Using a generalised model of a parallel architecture, the EO model can readily be extended to a number of individuals using evolutionary population dynamics and concepts of self-organising criticality. These solutions are treated in a manner consistent with the EO model. That is, poorly performing solutions can be replaced by random ones. The performance of standard EO and the new system shows that it is capable of finding near optimal solutions efficiently to most of the test problems. Copyright © 2007 IEEE Inc. All rights reserved. Marcus Randall http://epublications.bond.edu.au/infotech_pubs/4 http://epublications.bond.edu.au/infotech_pubs/4 Thu, 11 May 2006 02:30:35 PDT Local search, in either best or first admissible form, generally suffers from poor solution qualities as search cannot be continued beyond locally optimal points. Even multiple start local search strategies can suffer this problem. Meta-heuristic search algorithms, such as simulated annealing and tabu search, implement often computationally expensive optimisation strategies in which local search becomes a subordinate heuristic. To overcome this, a new form of local search is proposed. The Probabilistic Heuristic In Local (PHIL) search meta- strategy uses a recursive branching mechanism in order to overcome local optima. This strategy imposes only a small computational load over and above classical local search. A comparison between PHIL search and ant colony system on benchmark travelling salesman problem instances suggests that the new meta-strategy provides competitive performance. Extensions and improvements to the paradigm are also given. Marcus Randall