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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 e-Science Workshops, 2010
    Co-Authors: Marcus Randall, Andrew Lewis
    Abstract:

    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 e-Science, 2007
    Co-Authors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V Thiel
    Abstract:

    Increasing the efficiency of meander line Antennas is an importAnt real-world problem within radio frequency identification (RFID). Meta-heuristic 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 real-world problem.

  • Solution approaches for the capacitated single allocation hub location problem using Ant Colony Optimisation
    Computational Optimization and Applications, 2007
    Co-Authors: Marcus Randall
    Abstract:

    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 a-priori. Four variations of the Ant Colony Optimisation meta-heuristic 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.

  • eScience - Using Ant Colony Optimisation to Improve the Efficiency of Small Meander Line RFID Antennas
    Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007), 2007
    Co-Authors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V Thiel
    Abstract:

    Increasing the efficiency of meander line Antennas is an importAnt real-world problem within radio frequency identification (RFID). Meta-heuristic 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 real-world problem.

  • near parameter free Ant Colony Optimisation
    Ant Colony Optimization and Swarm Intelligence, 2004
    Co-Authors: Marcus Randall
    Abstract:

    Ant Colony Optimisation, like all other meta-heuristic search processes, requires a set of parameters in order to solve combinatorial problems. These parameters are often tuned by hand by the researcher to a set that seems to work well for the problem under study or a standard set from the literature. However, it is possible to integrate a parameter search process within the running of the meta-heuristic without incurring an undue computational overhead. In this paper, Ant Colony Optimisation is used to evolve suitable parameter values (using its own Optimisation processes) while it is solving combinatorial problems. The results reveal for the travelling salesman and quadratic assignment problems that the use of the augmented solver generally performs well against one that uses a standard set of parameter values. This is attributed to the fact that parameter values suitable for the particular problem instance can be automatically derived and varied throughout the search process.

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, 2012
    Co-Authors: Christopher Beer, Tim Hendtlass, James Montgomery
    Abstract:

    Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic). This paper details the development of Antennation, a mid-term 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 Multi-Tour 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, 2002
    Co-Authors: Marcus Randall, James Montgomery
    Abstract:

    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, 2002
    Co-Authors: Marcus Randall, James Montgomery
    Abstract:

    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.

Ansgar Kellner - One of the best experts on this subject based on the ideXlab platform.

  • Multi-objective Ant Colony Optimisation in Wireless Sensor Networks
    Nature-Inspired Computing and Optimization, 2017
    Co-Authors: Ansgar Kellner
    Abstract:

    Biologically inspired Ant Colony Optimisation (ACO) has been used in several applications to solve NP-hard combinatorial Optimisation problems. An interesting area of application for ACO-based algorithms is their use in wireless sensor networks (WSNs). Due to their robustness and self-organisation, ACO-based algorithms are well-suited for the distributed, autonomous and self-organising structure of WSNs. While the original ACO-based algorithm and its direct descendAnts can take only one objective into account, multi-objective Ant Colony Optimisation (MOACO) is capable of considering multiple (conflicting) objectives simultaneously. In this chapter, a detailed review and summary of MOACO-based algorithms and their applications in WSNs is given. In particular, a taxonomy of MOACO-based algorithms is presented and their suitability for multi-objective combinatorial Optimisation problems in WSNs is highlighted.

  • Multi-objective Ant Colony Optimisation-based routing in WSNs
    International Journal of Bio-Inspired Computation, 2014
    Co-Authors: Ansgar Kellner, Dieter Hogrefe
    Abstract:

    Wireless sensor networks (WSNs) are expected to play an importAnt role for sensing applications in the near future. A simple but robust way for the routing in WSNs is the use of multi-objective Ant Colony Optimisation (MOACO) algorithms, biologically-inspired algorithms that are capable of considering multiple objectives in the Optimisation process at the same time. In this paper MARFWSN, a multi-objective Ant Colony Optimisation routing framework for WSNs, is proposed that allows to use MOACO algorithms for the routing in WSNs. Due to its modulised structure different MOACO algorithms can be simply docked using the provided interface. Additionally, to mitigate insider attacks, trust is considered as one of the objectives in the route Optimisation process.

  • A Multi-objective Ant Colony Optimisation-based Routing Approach for Wireless Sensor Networks Incorporating Trust
    2012
    Co-Authors: Ansgar Kellner
    Abstract:

    In the near future, Wireless Sensor Networks (WSNs) are expected to play an importAnt role for sensing applications, in the civilian as well as in the military sector. WSNs are autonomous, distributed, self-organised networks consisting of multiple sensor nodes. Usually, the limited radio range of the nodes, arising from energy constrains, is overcome by the cooperation of nodes. As the Combinatorial Optimisation Problem (COP) of routing is computationally hard, often approximation algorithms are preferred, which are capable of finding near optimal solutions within polynomial time. A simple but robust way of solving the routing COP is the application of Ant Colony Optimisation (ACO)-based routing algorithms. When multiple (conflicting) objectives should be considered, ACO algorithms can be extended to Multi-objective Ant Colony Optimisation (MOACO) algorithms that are capable of considering multiple objectives at the same time within the Optimisation process. Normally, the routing in WSNs is susceptible to adversaries due to their deployment in unattended or in hostile environments. Particularly, attacks from compromised nodes (insider attacks) are a severe problem in WSNs. As insider attacks cannot be alleviated by classical security measures, often soft security measures (trust and reputation) are applied to mitigate the impact of these attacks. In this thesis, the idea of using trust as security measure against insider attacks is seized and interweaved with an MOACO-based routing approach. The Multi-objective Ant Colony Optimisation Routing Framework for WSNs (MARFWSN) is developed, a routing framework for WSNs that provides an interface for the docking of MOACO-based algorithms that can be used for the routing. Different MOACO-based algorithms

Andrew Lewis - 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 e-Science Workshops, 2010
    Co-Authors: Marcus Randall, Andrew Lewis
    Abstract:

    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 e-Science, 2007
    Co-Authors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V Thiel
    Abstract:

    Increasing the efficiency of meander line Antennas is an importAnt real-world problem within radio frequency identification (RFID). Meta-heuristic 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 real-world problem.

  • eScience - Using Ant Colony Optimisation to Improve the Efficiency of Small Meander Line RFID Antennas
    Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007), 2007
    Co-Authors: Marcus Randall, Andrew Lewis, Amir Galehdar, David V Thiel
    Abstract:

    Increasing the efficiency of meander line Antennas is an importAnt real-world problem within radio frequency identification (RFID). Meta-heuristic 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 real-world problem.