Stochastic Tabu Search strategy and its global convergence

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

2 Scopus Citations
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Original languageEnglish
Pages (from-to)410-414
Journal / PublicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
Publication statusPublished - 1997

Conference

TitleProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5)
CityOrlando, FL, USA
Period12 - 15 October 1997

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 a stochastic TS strategy for discrete optimization and makes an investigation of its global convergence. The strategy introduces the Metropolis criterion and annealing process of simulated annealing technology 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 asymptotical convergence. Furthermore, it produces a higher convergent rate than simulated annealing algorithm.