Abstract
An A-Team is a system of autonomous agents and the common memory. Each agent possesses some problem-solving skills and the memory contains a population of problem solutions. Cyclically solutions are being sent from the common memory to agents and from agents back to the common memory. Agents cooperate through selecting and modifying these solutions according to the user-defined strategy referred to as the working strategy. The modifications can be constructive or destructive. An attempt to improve a solution can be successful or unsuccessful. Agents can work asynchronously (each at its own speed) and in parallel. The A-Team working strategy includes a set of rules for agent communication, selection of solution to be improved and management of the population of solutions which are kept in the common memory. In this paper influence of different strategies on A-Team performance is investigated. To implement various strategies the A-Team platform called JABAT has been used. Different working strategies with respect to selecting solutions to be improved by the A-Team members and replacing the solutions stored in the common memory by the improved ones are studied. To evaluate typical working strategies the computational experiment has been carried out using several benchmark data sets. The experiment shows that designing effective working strategy can considerably improve the performance of the A-Team system.
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Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I. (2010). Influence of the Working Strategy on A-Team Performance. In: Szczerbicki, E., Nguyen, N.T. (eds) Smart Information and Knowledge Management. Studies in Computational Intelligence, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04584-4_4
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DOI: https://doi.org/10.1007/978-3-642-04584-4_4
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