Abstract
This paper presents a comparative analyzes between three search algorithms, named Fish School Search, Particle Swarm Optimization and Covariance Matrix Adaptation Evolution Strategy applied to ill-conditioned problems. We aim to demonstrate the effectiveness of the Fish School Search in the optimization processes when the objective function has ill-conditioned properties. We achieved good results for the Fish School Search and in some cases we obtained superior results when compared to the other algorithms.
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da C.C. Lins, A.J., Lima-Neto, F.B., Fages, F., Bastos-Filho, C.J.A. (2012). A Comparative Analysis of FSS with CMA-ES and S-PSO in Ill-Conditioned Problems. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_51
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DOI: https://doi.org/10.1007/978-3-642-32639-4_51
Publisher Name: Springer, Berlin, Heidelberg
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