Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Cooling schedules for optimal annealing
Mathematics of Operations Research
Integrating and accelerating tabu search, simulated annealing, and genetic algorithms
Annals of Operations Research - Special issue on Tabu search
On the convergence of generalized hill climbing algorithms
Discrete Applied Mathematics
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Finite-Time Performance Analysis of Static Simulated Annealing Algorithms
Computational Optimization and Applications
Simulated Annealing: Searching for an Optimal Temperature Schedule
SIAM Journal on Optimization
Simulated Annealing With An Optimal Fixed Temperature
SIAM Journal on Optimization
Analysis of static simulated annealing algorithms
Journal of Optimization Theory and Applications
A class of convergent generalized hill climbing algorithms
Applied Mathematics and Computation
A convergence analysis of generalized hill climbing algorithms
A convergence analysis of generalized hill climbing algorithms
Analyzing the Performance of Generalized Hill Climbing Algorithms
Journal of Heuristics
Analyzing the performance of simultaneous generalized hill climbing algorithms
Computational Optimization and Applications
INFORMS Journal on Computing
A framework for analyzing sub-optimal performance of local search algorithms
Computational Optimization and Applications
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Generalized hill climbing algorithms provide a framework for modeling several local search algorithms for hard discrete optimization problems. This paper introduces and analyzes generalized hill climbing algorithm performance measures that reflect how effectively an algorithm has performed to date in visiting a global optimum and how effectively an algorithm may perform in the future in visiting such a solution. These measures are also used to obtain a necessary asymptotic convergence (in probability) condition to a global optimum, which is then used to show that a common formulation of threshold accepting does not converge. These measures assume particularly simple forms when applied to specific search strategies such as Monte Carlo search and threshold accepting.