Abstract
This work presents two parallelization schemes applied to three different Multi-Objective Ant Colony Optimization (MOACO) algorithms. The aim is to get a better performance, improving the quality, quantity and the spread of solutions over the Pareto Front (the ideal set of solutions), rather than just reduce the running time. Colony-level (coarse-grained) implementations have been tested for solving two instances of the Bi-criteria TSP problem, yielding better sets of solutions, in the mentioned sense, than the correspondent sequential approach.
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Mora, A.M., Castillo, P.A., Arenas, M.G., García-Sánchez, P., Laredo, J.L.J., Merelo, J.J. (2012). Parallel Approaches in MOACOs for Solving the Bi-criteria TSP: A Preliminary Study. In: Kołodziej, J., Khan, S., Burczy´nski, T. (eds) Advances in Intelligent Modelling and Simulation. Studies in Computational Intelligence, vol 422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30154-4_3
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