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Ant Colony Optimisation
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Marcus Randall – One of the best experts on this subject based on the ideXlab platform.

Modifications and Additions to Ant Colony Optimisation to Solve the Set Partitioning Problem
2010 Sixth IEEE International Conference on eScience Workshops, 2010CoAuthors: Marcus Randall, Andrew LewisAbstract:Ant Colony Optimisation has traditionally been used to solve problems that have few/light constraints or no constraints at all. Algorithms to maintain and restore feasibility have been successfully applied to such problems. Set partitioning is a very constrained combinatorial Optimisation problem, for which even feasible solutions are difficult to construct. In this paper a binary Ant Colony Optimisation framework is applied to this problem. To increase its effectiveness, feasibility restoration, solution improvement algorithms and candidate set strategies are added. These algorithms can be applied to complete solution vectors and as such can be used by any solver. Moreover, the principles of the support algorithms may be applied to other constrained problems. The overall results indicate that the Ant Colony Optimisation algorithm can efficiently solve small to medium sized problems. It is envisaged that in future research parallel computation could be used to simultaneouly reduce solver time while increasing solution quality.

using Ant Colony Optimisation to improve the efficiency of small meander line rfid Antennas
International Conference on eScience, 2007CoAuthors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V ThielAbstract:Increasing the efficiency of meander line Antennas is an importAnt realworld problem within radio frequency identification (RFID). Metaheuristic search algorithms, such as Ant Colony Optimisation, are very efficient at solving problems that require paths to be constructed. This search technique is adapted to solve the grid based path problem for meander line Antennas and incorporates the NEC evaluation suite. The results for grid sizes up to 10 times 10 grid indicates that Ant Colony Optimisation is extremely effective at this realworld problem.

Solution approaches for the capacitated single allocation hub location problem using Ant Colony Optimisation
Computational Optimization and Applications, 2007CoAuthors: Marcus RandallAbstract:Hub and spoke type networks are often designed to solve problems that require the transfer of large quAntities of commodities. This can be an extremely difficult problem to solve for constructive approaches such as Ant Colony Optimisation due to the multiple Optimisation components and the fact that the quadratic nature of the objective function makes it difficult to determine the effect of adding a particular solution component. Additionally, the amount of traffic that can be routed through each hub is constrained and the number of hubs is not known apriori. Four variations of the Ant Colony Optimisation metaheuristic that explore different construction modelling choices are developed. The effects of solution component assignment order and the form of local search heuristics are also investigated. The results reveal that each of the approaches can return optimal solution costs in a reasonable amount of computational time. This may be largely attributed to the combination of integer linear preprocessing, powerful multiple neighbourhood local search heuristic and the good starting solutions provided by the Ant colonies.
James Montgomery – One of the best experts on this subject based on the ideXlab platform.

improving exploration in Ant Colony Optimisation with Antennation
Congress on Evolutionary Computation, 2012CoAuthors: Christopher Beer, Tim Hendtlass, James MontgomeryAbstract:Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one longterm (pheromone) and the other shortterm (local heuristic). This paper details the development of Antennation, a midterm heuristic based on an analogous process in real Ants. This is incorporated into ACO for the Travelling Salesman Problem (TSP). Antennation involves sharing information of the previous paths taken by Ants, including information gained from previous meetings. Antennation was added to the Ant System (AS), Ant Colony System (ACS) and Ant MultiTour System (AMTS) algorithms. Tests were conducted on symmetric TSPs of varying size. Antennation provides an advAntage when incorporated into algorithms without an inbuilt exploration mechanism and a disadvAntage to those that do. AS and AMTS with Antennation have superior performance when compared to their canonical form, with the effect increasing as problem size increases.

candidate set strategies for Ant Colony Optimisation
Lecture Notes in Computer Science, 2002CoAuthors: Marcus Randall, James MontgomeryAbstract:Ant Colony Optimisation based solvers systematically scan the set of possible solution elements before choosing a particular one. Hence, the computational time required for each step of the algorithm can be large. One way to overcome this is to limit the number of element choices to a sensible subset, or candidate set. This paper describes some novel generic candidate set strategies and tests these on the travelling salesman and car sequencing problems. The results show that the use of candidate sets helps to find competitive solutions to the test problems in a relatively short amount of time.

Ant Algorithms – Candidate Set Strategies for Ant Colony Optimisation
Ant Algorithms, 2002CoAuthors: Marcus Randall, James MontgomeryAbstract:Ant Colony Optimisation based solvers systematically scan the set of possible solution elements before choosing a particular one. Hence, the computational time required for each step of the algorithm can be large. One way to overcome this is to limit the number of element choices to a sensible subset, or candidate set. This paper describes some novel generic candidate set strategies and tests these on the travelling salesman and car sequencing problems. The results show that the use of candidate sets helps to find competitive solutions to the test problems in a relatively short amount of time.
David V Thiel – One of the best experts on this subject based on the ideXlab platform.

using Ant Colony Optimisation to improve the efficiency of small meander line rfid Antennas
International Conference on eScience, 2007CoAuthors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V ThielAbstract:Increasing the efficiency of meander line Antennas is an importAnt realworld problem within radio frequency identification (RFID). Metaheuristic search algorithms, such as Ant Colony Optimisation, are very efficient at solving problems that require paths to be constructed. This search technique is adapted to solve the grid based path problem for meander line Antennas and incorporates the NEC evaluation suite. The results for grid sizes up to 10 times 10 grid indicates that Ant Colony Optimisation is extremely effective at this realworld problem.

eScience – Using Ant Colony Optimisation to Improve the Efficiency of Small Meander Line RFID Antennas
Third IEEE International Conference on eScience and Grid Computing (eScience 2007), 2007CoAuthors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V ThielAbstract:Increasing the efficiency of meander line Antennas is an importAnt realworld problem within radio frequency identification (RFID). Metaheuristic search algorithms, such as Ant Colony Optimisation, are very efficient at solving problems that require paths to be constructed. This search technique is adapted to solve the grid based path problem for meander line Antennas and incorporates the NEC evaluation suite. The results for grid sizes up to 10 times 10 grid indicates that Ant Colony Optimisation is extremely effective at this realworld problem.