论文信息 - Convergence Analysis of Adaptive Tabu Search

Convergence Analysis of Adaptive Tabu Search

This paper presents a convergence proof of the adaptive tabu search (ATS) algorithms. The proof consists of two parts, i.e. convergence proof of all interested solutions in a finite search space, and that of searching processes of the ATS algorithms to the global minimum. With the proposed definitions and theorems, the proofs show that the ATS algorithms based on a random process have finite convergence. The searching process also converges to the (near) global minimum rapidly. Two applications, the global minimum finding of Bohachevsky’s function and the identification of the nonlinear pendulum model, serve to illustrate the effectiveness of the ATS algorithms.

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