The continuous reactive Tabu search: Global optimization with a hybrid of combinatorial optimization and stochastic local search

A novel algorithm for the global optimization of functions (C-RTS) is presented, in which a combinatorial optimization method cooperates with a stochastic local minimizer. The combinatorial optimization component, based on the Reactive Tabu Search proposed by the authors and based on Glover`s Tabu Search, locates the most promising {open_quotes}boxes{close_quotes}, where starting points for the local minimizer are generated. The method is designed with adaptive mechanisms: the box size is adapted to the local structure of the function to be optimized, the search parameters are adapted to obtain a proper balance of exploration and exploitation. The RTS algorithm is briefly described, with the related software engineering and VLSI realization projects. Then the modifications to obtain C-RTS are motivated and discussed, and some experimental results are presented for a suite of benchmark tasks.