Abstract
We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provides rapid convergence to good solutions while the particle swarm algorithm enables a comprehensive exploration of the search space. We provide experimental evidence that shows that the algorithm can find good solutions very rapidly without compromising its global search capabilities.
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Montes de Oca, M.A., Van den Enden, K., Stützle, T. (2008). Incremental Particle Swarm-Guided Local Search for Continuous Optimization. In: Blesa, M.J., et al. Hybrid Metaheuristics. HM 2008. Lecture Notes in Computer Science, vol 5296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88439-2_6
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DOI: https://doi.org/10.1007/978-3-540-88439-2_6
Publisher Name: Springer, Berlin, Heidelberg
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