Generalized Hill Climbing Algorithms For Discrete Optimization Problems
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Generalized Hill Climbing Algorithms For Discrete Optimization Problems

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Published by Storming Media .
Written in English


  • BUS049000

Book details:

The Physical Object
ID Numbers
Open LibraryOL11852661M
ISBN 101423584317
ISBN 109781423584315

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Generalized hill climbing (GHC) algorithms provide a general local search strategy to address intractable discrete optimization problems. GHC algorithms include as special cases stochastic local. This paper introduces simultaneous generalized hill-climbing (SGHC) algorithms as a framework for simultaneously addressing a set of related discrete optimization problems using heuristics. Many well-known heuristics can be embedded within the SGHC algorithm framework, including simulated annealing, pure local search, and threshold accepting (among others).Cited by: 8. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing Cited by: Simulated annealing is a popular local search meta-heuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a means to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a Cited by:

Figure Example of enforced hill-climbing (two iterations). Black nodes are expanded within the BFS, gray nodes are exit states. The first BFS iteration (left), starting at the root, with an h-value 2, generates a successor of a smaller h-value 1 second BFS iteration (right) searches for a node with an h-value smaller than generates the goal, so that the algorithm. Mathematical optimization (alternatively spelt optimisation) or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of.   Simultaneous Generalized Hill-Climbing Algorithms for Addressing Sets of Discrete Optimization Problems Diane E. Vaughan, Sheldon H. Jacobson, Shane N. Hall, . Computing Methods in Optimization Problems deals with hybrid computing methods and optimization techniques using computers. One paper discusses different numerical approaches to optimizing trajectories, including the gradient method, the second variation method, and a generalized Newton-Raphson method.

Greedy algorithms determine minimum number of coins to give while making change. These are the steps a human would take to emulate a greedy algorithm to represent 36 cents using only coins with values {1, 5, 10, 20}. The coin of the highest value, less than the remaining change owed, is the local optimum. (In general the change-making problem. SIAM Journal on Control and Optimization > Vol Issue 1 > / Regularity and Numerical Solution of Eigenvalue Problems with Piecewise Analytic Data Analyzing the performance of simultaneous generalized hill climbing algorithms. Computational Optimization and Cited by: Jacobson S and Yücesan E () Analyzing the Performance of Generalized Hill Climbing Algorithms, Journal of Heuristics, , (), Online publication date: 1-Jul Armentano V and Claudio J () An Application of a Multi-Objective Tabu Search Algorithm to a Bicriteria Flowshop Problem, Journal of Heuristics, , ( The Newton-CG method is a line search method: it finds a direction of search minimizing a quadratic approximation of the function and then uses a line search algorithm to find the (nearly) optimal step size in that direction. An alternative approach is to, first, fix the step size limit \ (\Delta\) and then find the optimal step \ (\mathbf {p.