FITTING OF TABU SEARCH TO OPTIMIZE FUNCTIONS OF CONTINUOUS VARIABLES

Tabu Search (TS) is a stochastic global optimization procedure which proved e¦cient to solve various combinatorial optimization problems. However, very few works deal with its application to global minimization of functions depending on continuous variables. The aim of this paper is to propose an adaptation of TS to the optimization of continuous functions, and to study the inßuence of the main algorithm parameters on the convergence towards the optimum. In particular, the application of TS to function optimization involves the deÞnition of the current solution neighbourhood and the management of the tabu list. The e¦ciency of TS applied to continuous global optimization has been tested in detail by using classical multimodal functions for which minima are known