Cooling schedules for optimal annealing
Mathematics of Operations Research
Job shop scheduling by simulated annealing
Operations Research
Insertion techniques for the heuristic solution of the job shop problem
Proceedings of the workshop on Discrete algorithms
A fast taboo search algorithm for the job shop problem
Management Science
Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Artificial Intelligence
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Tabu Search
Problem difficulty for tabu search in job-shop scheduling
Artificial Intelligence
A mixture-model for the behaviour of SLS algorithms for SAT
Eighteenth national conference on Artificial intelligence
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Backbone fragility and the local search cost peak
Journal of Artificial Intelligence Research
Backbones in optimization and approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A memetic genetic algorithm for the vertex p-center problem
Evolutionary Computation
A population based hybrid meta-heuristic for the uncapacitated facility location problem
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
Optimal Task Migration in Service-Oriented Systems: Algorithms and Mechanisms
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
Artificial Intelligence
NuMVC: an efficient local search algorithm for minimum vertex cover
Journal of Artificial Intelligence Research
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Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.