Results 1  10
of
55
Variable Neighborhood Search
, 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 342 (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 are briefly summarized. They comprise heuristic solution of a variety of optimization problems, ways to accelerate exact algorithms and to analyze heuristic solution processes, as well as computerassisted discovery of conjectures in graph theory.
Variable neighborhood search: Principles and applications
, 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 180 (16 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 any local search algorithm as a subroutine. Its effectiveness is illustrated by solving several classical combinatorial or global optimization problems. Moreover, several extensions are proposed for solving large problem instances: using VNS within the successive approximation method yields a twolevel VNS, called variable neighborhood decomposition search (VNDS); modifying the basic scheme to explore easily valleys far from the incumbent solution yields an efficient skewed VNS (SVNS) heuristic. Finally, we show how to stabilize column generation algorithms with help of VNS and discuss various ways to use VNS in graph theory, i.e., to suggest, disprove or give hints on how to prove conjectures, an area where metaheuristics do not appear
A Hybrid Heuristic for the pMedian Problem
, 2003
"... Given n customers and a set F of m potential facilities, the pmedian problem consists in finding a subset of F with p facilities such that the cost of serving all customers is minimized. ..."
Abstract

Cited by 46 (11 self)
 Add to MetaCart
Given n customers and a set F of m potential facilities, the pmedian problem consists in finding a subset of F with p facilities such that the cost of serving all customers is minimized.
The pmedian problem: A survey of metaheuristic approaches
 European J Operational Research 179 927
, 2007
"... The pmedian problem, like most location problems, is classified as NPhard, and so, heuristic methods are usually used for solving it. The pmedian problem is a basic discrete location problem with real application that have been widely used to test heuristics. Metaheuristics are frameworks for bui ..."
Abstract

Cited by 25 (2 self)
 Add to MetaCart
The pmedian problem, like most location problems, is classified as NPhard, and so, heuristic methods are usually used for solving it. The pmedian problem is a basic discrete location problem with real application that have been widely used to test heuristics. Metaheuristics are frameworks for building heuristics. In this survey, we examine the pmedian, with the aim of providing an overview on advances in solving it using recent procedures based on metaheuristic rules.
Solving the pCenter Problem with Tabu Search and Variable Neighborhood Search
, 2000
"... The pCenter problem consists in locating p facilities and assigning clients to them in order to minimize the maximum distance between a client and the facility to which he is allocated. In this paper we present a basic Variable Neighborhood Search and two Tabu Search heuristics for the pCenter ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
The pCenter problem consists in locating p facilities and assigning clients to them in order to minimize the maximum distance between a client and the facility to which he is allocated. In this paper we present a basic Variable Neighborhood Search and two Tabu Search heuristics for the pCenter problem without triangle inequality. Both proposed methods use the 1interchange (or vertex substitution) neighborhood structure.
On the Implementation of a SwapBased Local Search Procedure for the pMedian Problem
, 2002
"... We present a new implementation of a widely used swapbased local search procedure for the pmedian problem. It produces the same output as the best implementation described in the literature and has the same worstcase complexity, but, through the use of extra memory, it can be significantly faster ..."
Abstract

Cited by 19 (6 self)
 Add to MetaCart
(Show Context)
We present a new implementation of a widely used swapbased local search procedure for the pmedian problem. It produces the same output as the best implementation described in the literature and has the same worstcase complexity, but, through the use of extra memory, it can be significantly faster in practice: speedups of up to three orders of magnitude were observed.
Fuzzy JMeans: a new heuristic for fuzzy clustering
, 2002
"... A fuzzy clustering problem consists of assigning a set of patterns to a given number of clusters with respect to some criteria such that each of them may belong to more than one cluster with different degrees of membership. In order to solve it, we first propose a new local search heuristic, called ..."
Abstract

Cited by 16 (5 self)
 Add to MetaCart
A fuzzy clustering problem consists of assigning a set of patterns to a given number of clusters with respect to some criteria such that each of them may belong to more than one cluster with different degrees of membership. In order to solve it, we first propose a new local search heuristic, called FuzzyJMeans, where the neighbourhood is defined by all possible centroidtopattern relocations. The “integer” solution is then moved to a continuous one by an alternate step, i.e., by finding centroids and membership degrees for all patterns and clusters. To alleviate the difficulty of being stuck in local minima of poor value, this local search is then embedded into the Variable Neighbourhood Search metaheuristic. Results on five standard test problems from the literature are reported and compared with those obtained with the wellknown FuzzyCMeans heuristic. It appears that solutions of substantially better quality are obtained with the proposed methods than with this former one.
A Tutorial on Variable Neighborhood Search
 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 the ingredients of VNS, i.e., Variable Neighborhood Descent (VND) and Reduced VNS (RVNS) followed by the basic and then the general scheme of VNS itself which contain both of them. Extensions are presented, in particular Skewed VNS (SVNS) which enhances exploration of far away valleys and Variable Neighborhood Decomposition Search (VNDS), a twolevel scheme for solution of large instances of various problems. In each case, we present the scheme, some illustrative examples and questions to be addressed in order to obtain an efficient implementation.
Node Ejection Chains for the Vehicle Routing Problem: Sequential and Parallel Algorithms
, 1997
"... this paper is to describe a new tabu search algorithm for the general VRP defined above. Tabu search is a metaheuristic proposed by Glover [13]. The method is generically presented in Glover [14,15] and recent developments may be found in Glover [18,19] (see Glover and de Werra [22], Glover [21] for ..."
Abstract

Cited by 15 (3 self)
 Add to MetaCart
this paper is to describe a new tabu search algorithm for the general VRP defined above. Tabu search is a metaheuristic proposed by Glover [13]. The method is generically presented in Glover [14,15] and recent developments may be found in Glover [18,19] (see Glover and de Werra [22], Glover [21] for a survey on tabu search applications and challenges). A number of algorithms based on this approach have already been applied to the VRP, each one using different types of moves leading from one solution to another (see, Pureza and Franca [33], Osman [32], Taillard [40], Gendreau, Hertz and Laporte [11], Rochat and Taillard [38], Rego [36], Xu and Kelly [41]). An important contribution of our method is the use of embedded neighborhood structures based on the idea of ejection chains. Embedded neighborhoods may be conceived as the outcome of compressing a sequence of moves into a single compound move, and ejection chain procedures give a useful way to build these neighborhoods. For a comprehensive description of ejection chain methods we refer to Glover [20,16] and Rego [34]. A number of methods based on this prespective have recently been proposed for various combinatorial problems (see Laguna et al. [28], Dorndorf and Pesch [8], Hubscher and Glover [27], Rego [35,36], Glover, Pesch and Osman [24], Cao and Glover [2]). The remainder of this paper is organized as follows. In section 2, we briefly summarize the ideas underlying ejection chains and we describe their application to the VRP. Section 3 describes the sequential version of the proposed algorithm and a parallel approach is described in section 4. Then, the computational results and a comparative analysis of the algorithms are presented in section 5. Finally, section 6 contains a summary and concluding remarks. 2 New...
Parallelization of the scatter search for the pmedian problem
 Parallel Computing
, 2003
"... q ..."
(Show Context)