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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 ..."
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Cited by 341 (25 self)
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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 New Effective Local Search Heuristic for the Maximum clique problem
"... Abstract—An edge based local search algorithm, called ELS, is proposed for the maximum clique problem (MCP), a wellknown combinatorial optimization problem. ELS is a two phased local search method effectively £nds the near optimal solutions for the MCP. A parameter ’support ’ of vertices de£ned in ..."
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Abstract—An edge based local search algorithm, called ELS, is proposed for the maximum clique problem (MCP), a wellknown combinatorial optimization problem. ELS is a two phased local search method effectively £nds the near optimal solutions for the MCP. A parameter ’support ’ of vertices de
A Scatter Search Algorithm for the Maximum Clique Problem
, 2001
"... Abstract – The objective of the Maximum Clique Problem (MCP) is to find the largest complete subgraph in a given graph. The problem is known as NPhard and we have developed a heuristic algorithm based on a Scatter Search (SS) framework to find a lower bound for this maximization problem. The propos ..."
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Abstract – The objective of the Maximum Clique Problem (MCP) is to find the largest complete subgraph in a given graph. The problem is known as NPhard and we have developed a heuristic algorithm based on a Scatter Search (SS) framework to find a lower bound for this maximization problem
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 ..."
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Cited by 185 (15 self)
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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
Reactive Local Search for the Maximum Clique Problem
 Algorithmica
"... A new Reactive Local Search (RLS ) algorithm is proposed for the solution of the MaximumClique problem. RLS is based on local search complemented by a feedback (historysensitive) scheme to determine the amount of diversification. The reaction acts on the single parameter that decides the temporary ..."
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Cited by 95 (14 self)
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A new Reactive Local Search (RLS ) algorithm is proposed for the solution of the MaximumClique problem. RLS is based on local search complemented by a feedback (historysensitive) scheme to determine the amount of diversification. The reaction acts on the single parameter that decides
Using constraint programming to solve the maximum clique problem
 Principles and Practice of Constraint Programming  CP 2003, LNCS 2833
, 2003
"... Abstract. This paper aims to show that Constraint Programming can be an efficient technique to solve a wellknown combinatorial optimization problem: the search for a maximum clique in a graph. A clique of a graph G = (X, E) is a subset V of X, such that every two nodes in V are joined by an edge of ..."
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Cited by 16 (1 self)
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Abstract. This paper aims to show that Constraint Programming can be an efficient technique to solve a wellknown combinatorial optimization problem: the search for a maximum clique in a graph. A clique of a graph G = (X, E) is a subset V of X, such that every two nodes in V are joined by an edge
Phase Transitions of Dominating Clique Problem and Their Implications to Heuristics in Satisfiability Search
"... We study a monotone NP decision problem, the dominating clique problem, whose phase transition occurs at a very dense stage of the random graph evolution process. We establish the exact threshold of the phase transition and propose an efficient search algorithm that runs in superpolynomial time wit ..."
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Cited by 5 (2 self)
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We study a monotone NP decision problem, the dominating clique problem, whose phase transition occurs at a very dense stage of the random graph evolution process. We establish the exact threshold of the phase transition and propose an efficient search algorithm that runs in superpolynomial time
An Effective Local Search for the Maximum Clique Problem
"... We propose a variable depth search based algorithm, called kopt local search (KLS), for the maximum clique problem. KLS efficiently explores the kopt neighborhood defined as the set of neighbors that can be obtained by a sequence of several add and drop moves that are adaptively changed in the fea ..."
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Cited by 16 (2 self)
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We propose a variable depth search based algorithm, called kopt local search (KLS), for the maximum clique problem. KLS efficiently explores the kopt neighborhood defined as the set of neighbors that can be obtained by a sequence of several add and drop moves that are adaptively changed
A penaltyevaporation heuristic in a decomposition method for the maximum clique problem
 IN OPTIMIZATION DAYS
, 2003
"... In this paper, we present a heuristic method to solve the maximum clique problem, based on the concepts of penalty and evaporation. At each iteration, some vertex i is inserted into the current solution (always a clique) and the vertices that are not adjacent to vertex i are removed from the solutio ..."
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Cited by 2 (0 self)
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In this paper, we present a heuristic method to solve the maximum clique problem, based on the concepts of penalty and evaporation. At each iteration, some vertex i is inserted into the current solution (always a clique) and the vertices that are not adjacent to vertex i are removed from
Results 1  10
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