Tabu Search applied to global optimization

Abstract

A new algorithm called Enhanced Continuous Tabu Search (ECTS) is proposed for the global optimization of multiminima functions. It results from an adaptation of combinatorial Tabu Search which aims to follow, as close as possible, Glover's basic approach. In order to cover a wide domain of possible solutions, our algorithm first performs the diversification: it locates the most promising areas, by fitting the size of the neighborhood structure to the objective function and its definition domain. When the most promising areas are located, the algorithm continues the search by intensification within one promising area of the solution space. The efficiency of ECTS is thoroughly tested by using a set of benchmark multimodal functions, of which global and local minima are known. ECTS is compared to other published versions of continuous Tabu Search and to some alternative algorithms like Simulated Annealing. We point out two main advantages of ECTS: first its principle is rather basic, directly inspired from combinatorial Tabu Search; secondly it shows a good performance for functions having a large number of variables (more than 10).