Simulated annealing: theory and applications
Simulated annealing: theory and applications
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Journal of Computational Physics
Simulated annealing: past, present, and future
WSC '95 Proceedings of the 27th conference on Winter simulation
Computational issues for accessibility in discrete event simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Classification of acceptance criteria for the simulated annealing algorithm
Mathematics of Operations Research
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Generalized hill-climbing algorithms for discrete optimization problems
Generalized hill-climbing algorithms for discrete optimization problems
Finite-Time Performance Analysis of Static Simulated Annealing Algorithms
Computational Optimization and Applications
Ordinal Hill Climbing Algorithms for Discrete ManufacturingProcess Design Optimization Problems
Discrete Event Dynamic Systems
Analysis of static simulated annealing algorithms
Journal of Optimization Theory and Applications
Analyzing the Performance of Generalized Hill Climbing Algorithms
Journal of Heuristics
Global Optimization Performance Measures for Generalized Hill Climbing Algorithms
Journal of Global Optimization
Analyzing the performance of simultaneous generalized hill climbing algorithms
Computational Optimization and Applications
INFORMS Journal on Computing
International Journal of Computer Applications in Technology
Adaptive parameterized improving hit-and-run for global optimization
Optimization Methods & Software - GLOBAL OPTIMIZATION
Hill climbing algorithms and Trivium
SAC'10 Proceedings of the 17th international conference on Selected areas in cryptography
A framework for analyzing sub-optimal performance of local search algorithms
Computational Optimization and Applications
Adaptive search with stochastic acceptance probabilities for global optimization
Operations Research Letters
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Generalized hill climbing (GHC) algorithms have been presented as a modeling framework for local search strategies applied to address intractable discrete optimization (minimization) problems. GHC algorithms include simulated annealing (SA), pure local search (LS), and threshold accepting (TA), among others, as special cases. A particular class of GHC algorithms is designed for discrete optimization problems where the objective function value of a globally optimal solution is known (in this case, the task is to identify an associated optimal solution). This class of GHC algorithms is shown to converge, and six examples are provided that illustrate the diversity of GHC algorithms within this class of convergent algorithms. Implications of these results are discussed.