A novel algorithm for the global optimisation of functions (C-RTS) is presented, in which a combinatorial optimisation method cooperates whit a stochastic local minimizer. The combinatorial optimisation component, based on the Reactive Tabu Search recently proposed by the authors, locates the most promising 'boxes', where starting points for the local minimizer are generated. In order to cover a wide spectrum of possible applications with no user intervention, the method is designed with adaptive mechanisms: the box size is adapted to the local structure of the fnction to be optimized, the search parameters are adapted to obtain a proper balance of diversification and intensification. The algorithm is compared with some existing algorithms, and the experimental results are presented for a suite of benchmark tasks

The Continuous Reactive Tabu Search: Blending Combinatorial Optimization Stochastic Search for Global Optimzation

1996

Abstract

A novel algorithm for the global optimisation of functions (C-RTS) is presented, in which a combinatorial optimisation method cooperates whit a stochastic local minimizer. The combinatorial optimisation component, based on the Reactive Tabu Search recently proposed by the authors, locates the most promising 'boxes', where starting points for the local minimizer are generated. In order to cover a wide spectrum of possible applications with no user intervention, the method is designed with adaptive mechanisms: the box size is adapted to the local structure of the fnction to be optimized, the search parameters are adapted to obtain a proper balance of diversification and intensification. The algorithm is compared with some existing algorithms, and the experimental results are presented for a suite of benchmark tasks
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/1047
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