Abstract
In this work, the parallelization of some Multi-Objective Ant Colony Optimization (MOACO) algorithms has been performed. The aim is to get a better performance, not only in running time (usually the main objective when a distributed approach is implemented), but also improving the spread of solutions over the Pareto front (the ideal set of solutions). In order to do this, colony-level (coarse- grained) implementations have been tested for solving the Bicriteria TSP problem, yielding better sets of solutions, in the sense explained above, than a sequential approach.
This work has been supported in part by HPC-Europa 2 project (with the support of the European Commission - Capacities Area - Research Infrastructures), by the CEI BioTIC GENIL (CEB09-0010) Programa CEI del MICINN (PYR-2010-13) project, the Junta de Andalucía TIC-3903 and P08-TIC-03928 projects, and the Jaén University UJA-08-16-30 project.
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Mora, A.M. et al. (2011). A Study of Parallel Approaches in MOACOs for Solving the Bicriteria TSP. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_40
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