Global search methods for nonlinear optimisation: a new probabilistic-stochastic approach
Document typeConference report
PublisherInternational Centre for Numerical Methods in Engineering (CIMNE)
Rights accessOpen Access
In this work the problem of overcoming local minima in the solution of nonlinear optimisation problems is addressed. As a first step, the existing nonlinear local and global optimisation methods are reviewed so as to identify their advantages and disadvantages. Then, the major capabilities of a number of successful methods such as genetic, deterministic global optimisation methods and simmulated annealing, are combined to develop an alternative global optimisation approach based on a Stochastic-Probabilistic heuristic. The capabilities, in terms of robustness and efficiency, of this new approach are validated through the solution of a number of nonlinear optimisation problems. A well know evolutionary technique (Differential Evolution) is also considered for the solution of these case studies offering a better insight of the possibilities of the method proposed here.
CitationBugeda, G., Balsa-Canto, E., Thamotheram, C., Oñate, E., Zarate, J. Global search methods for nonlinear optimisation: a new probabilistic-stochastic approach. A: Conference on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems. "Evolutionary methods for design, optimization and control: applications to industrial and societal problems: proceedings of the Fifth Conference on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems Barcelona, September 2003". Barcelona: International Centre for Numerical Methods in Engineering (CIMNE), 2003, p. 1-9.
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