A stochastic tabu search strategy and its global convergence

Abstract

Tabu search (TS) is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. It has achieved widespread successes in solving practical optimization problems. This paper proposes the stochastic TS strategy for discrete optimization and makes an investigation of its global convergence. The strategy considered introduces the Metropolis criterion and simulated annealing process into a general framework of TS. It has been proved that the strategy converges asymptotically to global optimal solutions, and satisfies the necessary and sufficient conditions for global asymptotic convergence. Furthermore, it produces a higher convergent rate than the simulated annealing algorithm.