Abstract
Most global optimization algorithms offer a trade-off in that they solve one class of problems better for the price of solving another class of problems worse. This is to be expected in light of theoretical results like No free lunch theorem. It is desirable, therefore, to have an automatic method of constructing algorithms tuned for solving specific problems and classes of problems. We offer a variant of Fully-Informed Particle Swarm Optimization algorithm that is highly tunable. We show how to use meta-optimization to optimize it’s neighbourhood space and influence function to adjust it to solving various test problems. The optimized neighbourhood configurations and influence functions also give insights in to what it takes for a Particle Swarm Optimization algorithm to successfully solve a problem. These configurations are often contrary to what people would design using their intuitions. This means that meta-optimization in this case can be used as a tool for scientific exploration as well as for practical utility.
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Jančauskas, V. (2014). Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_17
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DOI: https://doi.org/10.1007/978-3-319-01692-4_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01691-7
Online ISBN: 978-3-319-01692-4
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