A combined global & local search (CGLS) approach to global optimization This paper presents a general approach that combines global search strategies with local search and attempts to find a global minimum of a real valued function of $n$ variables. It assumes that derivative information is unreliable; consequently, it deals with derivative free algorithms, but derivative information can be easily incorporated. This paper presents a nonmonotone derivative free algorithm and shows numerically that it may converge to a better minimum starting from a local nonglobal minimum. This property is then incorporated into a random population to globalize the algorithm. Convergence to a zero order stationary point is established for nonsmooth convex functions, and convergence to a first order stationary point is established for strictly differentiable functions. Preliminary numerical results are encouraging. A Java implementation that can be run directly from the Web allows the interested reader to get a better insight of the performance of the algorithm on several standard functions. The general framework proposed here, allows the user to incorporate variants of well known global search strategies.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
- Custódio, A.L.; Madeira, J.F.A.: GLODS: global and local optimization using direct search (2015)
- Zhou, Qinghua; Li, Yan; Ha, Minghu: Constructing composite search directions with parameters in quadratic interpolation models (2011)
- Olenšek, Jernej; Bűrmen, Árpád; Puhan, Janez; Tuma, Tadej: DESA: a new hybrid global optimization method and its application to analog integrated circuit sizing (2009)
- González-Castaño, Francisco J.; Costa-Montenegro, Enrique; Burguillo-Rial, Juan C.; García-Palomares, Ubaldo: Outdoor WLAN planning via non-monotone derivative-free optimization: Algorithm adaptation and case study (2008)
- Lee, Chi-Hoon; Zaïane, Osmar R.; Park, Ho-Hyun; Huang, Jiayuan; Greiner, Russell: Clustering high dimensional data: A graph-based relaxed optimization approach (2008) ioport
- Garcia-Palomares, U.M.; Gonzalez-Castaño, F.J.; Burguillo-Rial, J.C.: A combined global & local search (CGLS) approach to global optimization (2006)