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Showing 1 to 4 of 4 for “"Generalized Hill Climbing Algorithms"”.
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Generalized hill climbing algorithms for discrete optimization problems
Generalized hill climbing (GHC) algorithms are introduced, as a tool to address difficult discrete optimization problems. Particular formulations of GHC algorithms include simulated annealing (SA), local search, and threshold accepting (T A), among. others. A proof of convergence of GHC algorithms …
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A Convergence Analysis of Generalized Hill Climbing Algorithms
Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence …
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Simultaneous Generalized Hill Climbing Algorithms for Addressing Sets of Discrete Optimization Problems
Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. Many well-known local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo …
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Land Leveling Using Optimal Earthmoving Vehicle Routing
This thesis presents new solution approaches for land leveling, using optimal earthmoving vehicle routing. It addresses the Shortest Route Cut and Fill Problem (SRCFP) developed by Henderson, Vaughan, Wakefield and Jacobson [2000]. The SRCFP is a discrete optimization search problem, proven to be …