Abstract
Metaheuristics are used successfully in several global optimization functions. Problems arise when functions have large flat regions since information, given by the slope, necessary to guide the search is insufficient. In such case, a common solution can be a change in the metaheuristic’s parameters in order to attain a optimal balance between the exploration and exploitation. In this paper, we propose a criterion to determine when a flat region can be problematic. It is validated with a very simple hybrid algorithm based on the use of PSO technique for optimizing non-flat regions and Monte Carlo sampling for searching the global optimum in large flat regions. The proposed criterion switches the both algorithms to provide more exploitation for descendent functions and more exploration for planar functions. Preliminary results show that the proposed hybrid algorithm finds better results than PSO and Monte Carlo techniques in isolation for ten well-known test functions.
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Mesa, E., Velásquez, J.D., Jaramillo, G.P. (2014). Nonlinear Optimization in Landscapes with Planar Regions. 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_16
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DOI: https://doi.org/10.1007/978-3-319-01692-4_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01691-7
Online ISBN: 978-3-319-01692-4
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