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Variable Neighborhood Search

by Pierre Hansen, Nenad Mladenovic , 1997
"... Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications a ..."
Abstract - Cited by 355 (26 self) - Add to MetaCart
Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications

A Hybrid Scatter Search Heuristic for Personalized Crew Rostering in the Airline Industry

by Broos Maenhout, Mario Vanhoucke , 2007
"... The crew scheduling problem in the airline industry is extensively investigated in the operations research literature since efficient crew employment can drastically reduce operational costs of airline companies. Given the flight schedule of an airline company, crew scheduling is the process of assi ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
characteristics of the procedure are presented. Moreover, we compare the proposed scatter search algorithm with an exact branch-and-price procedure and a steepest descent variable neighborhood search.

Variable neighborhood search: Principles and applications

by Pierre Hansen, Nenad Mladenovic , 2001
"... Systematic change of neighborhood within a possibly randomized local search algorithm yields a simple and effective metaheuristic for combinatorial and global optimization, called variable neighborhood search (VNS). We present a basic scheme for this purpose, which can easily be implemented using an ..."
Abstract - Cited by 198 (15 self) - Add to MetaCart
Systematic change of neighborhood within a possibly randomized local search algorithm yields a simple and effective metaheuristic for combinatorial and global optimization, called variable neighborhood search (VNS). We present a basic scheme for this purpose, which can easily be implemented using

Variable Neighborhood Decomposition Search

by Pierre Hansen, Nenad Mladenović, Dionisio Perez-Brito , 2001
"... The recent Variable Neighborhood Search (VNS) metaheuristic combines local search with systematic changes of neighborhood in the descent and escape from local optimum phases. When solving large instances of various problems, its efficiency may be enhanced through decomposition. The resulting two lev ..."
Abstract - Cited by 58 (11 self) - Add to MetaCart
The recent Variable Neighborhood Search (VNS) metaheuristic combines local search with systematic changes of neighborhood in the descent and escape from local optimum phases. When solving large instances of various problems, its efficiency may be enhanced through decomposition. The resulting two

Variable neighborhood descent with selfadaptive neighborhood-ordering

by Bin Hu, Günther R. Raidl - Proceedings of the 7th EU/MEeting on Adaptive, Self-Adaptive, and Multi-Level Metaheuristics , 2006
"... In Variable Neighborhood Descent (VND) it is often difficult to decide upon the ordering in which a different types of neighborhoods are considered. This arrangement typically strongly affects the quality of finally obtained solution as well as computation time. We present a VND variant which orders ..."
Abstract - Cited by 11 (2 self) - Add to MetaCart
In Variable Neighborhood Descent (VND) it is often difficult to decide upon the ordering in which a different types of neighborhoods are considered. This arrangement typically strongly affects the quality of finally obtained solution as well as computation time. We present a VND variant which

Phase retrieval algorithms: a comparison

by J. R. Fienup - Appl. Opt , 1982
"... Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sens-ing) and the problem of phase retrieval from a single intensity measurement plus a non- ..."
Abstract - Cited by 294 (15 self) - Add to MetaCart
to be closely re-lated to the steepest-descent method. Other algorithms, including the input-output algorithm and the con-jugate-gradient method, are shown to converge in practice much faster than the error-reduction algorithm. Examples are shown. 1.

v, ON THE METHOD OF STEEPEST DESCENTS

by P. B. Chapman
"... The method of steepest descents is the most usual technique used for constructing asymptotic representations of contour integrals o £ the form Jet f(z) g(z) dz where f(z) is analytic and t is large and real. In the method, the path of integration is deformed so as to pass through saddle points (i.e. ..."
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The method of steepest descents is the most usual technique used for constructing asymptotic representations of contour integrals o £ the form Jet f(z) g(z) dz where f(z) is analytic and t is large and real. In the method, the path of integration is deformed so as to pass through saddle points (i

SVM-optimization and steepest-descent line search

by Nikolas List, Hans Ulrich Simon - In Proceedings of the 21st Annual Conference on Learning Theory (COLT 2009 , 2009
"... We consider (a subclass of) convex quadratic optimization problems and analyze decomposition algorithms that perform, at least approximately, steepest-descent exact line search. We show that these algorithms, when implemented properly, are within ǫ of optimality after O(log 1/ǫ) iterations for stric ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
We consider (a subclass of) convex quadratic optimization problems and analyze decomposition algorithms that perform, at least approximately, steepest-descent exact line search. We show that these algorithms, when implemented properly, are within ǫ of optimality after O(log 1/ǫ) iterations

An Empirical Evaluation of O(1) Steepest Descent for NK-Landscapes

by Darrell Whitley, Wenxiang Chen, Adele Howe - Parallel Problem Solving from Nature - PPSN XII, volume 7491 of Lecture Notes in Computer Science , 2012
"... Abstract. New methods make it possible to do approximate steepest descent in O(1) time per move for k-bounded pseudo-Boolean functions using stochastic local search. It is also possible to use the average fit-ness over the Hamming distance 2 neighborhood as a surrogate fitness function and still ret ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract. New methods make it possible to do approximate steepest descent in O(1) time per move for k-bounded pseudo-Boolean functions using stochastic local search. It is also possible to use the average fit-ness over the Hamming distance 2 neighborhood as a surrogate fitness function and still

A Tutorial on Variable Neighborhood Search

by Pierre Hansen, Nenad Mladenović - LES CAHIERS DU GERAD, HEC MONTREAL AND GERAD , 2003
"... Variable Neighborhood Search (VNS) is a recent metaheuristic, or framework for building heuristics, which exploits systematically the idea of neighborhood change, both in the descent to local minima and in the escape from the valleys which contain them. In this tutorial we first present the ingre ..."
Abstract - Cited by 16 (3 self) - Add to MetaCart
Variable Neighborhood Search (VNS) is a recent metaheuristic, or framework for building heuristics, which exploits systematically the idea of neighborhood change, both in the descent to local minima and in the escape from the valleys which contain them. In this tutorial we first present
Results 1 - 10 of 688