Abstract
Particle swarm optimization (PSO) and ant-colony optimization (ACO) are novel multi-agent algorithms able to solve complex problem. By consequence, it would seem wise to compare their performances for solving such problems. For this purpose, both algorithms are compared together and with Matlab’s GA in term of accuracy of the solution and computation time. In this paper the optimization is applied on the design of a brushless DC wheel motor that is known as a nonlinear multimodal benchmark.
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Moussouni, F., Brisset, S., Brochet, P. (2008). Comparison of Two Multi-Agent Algorithms: ACO and PSO for the Optimization of a Brushless DC Wheel Motor. In: Wiak, S., Krawczyk, A., Dolezel, I. (eds) Intelligent Computer Techniques in Applied Electromagnetics. Studies in Computational Intelligence, vol 119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78490-6_1
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DOI: https://doi.org/10.1007/978-3-540-78490-6_1
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