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M. Montes de Oca, K. Van den Enden, and T. Stützle. Incremental Particle Swarm-Guided Local Search for Continuous Optimization. In M. J. Blesa, editor, Hybrid Metaheuristics, 5th International Workshop, HM 2008, Proceedings, volume 5296 of Lecture Notes in Computer Science, pages 72-86. Springer-Verlag, Berlin, Germany, 2008.

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.


Updated: 2017-03-27