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21
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 in the feasible search space. Computational results on DIMACS benchmark graphs indicate that KLS is capable of finding considerably satisfactory cliques with reasonable running times in comparison with those of stateoftheart metaheuristics.
Iterated Tabu Search for the Unconstrained Binary Quadratic Optimization Problem
, 2005
"... Abstract. Given a set of objects with profits (any, even negative, numbers) assigned not only to separate objects but also to pairs of them, the unconstrained binary quadratic optimization problem consists in finding a subset of objects for which the overall profit is maximized. In this paper, an it ..."
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Cited by 13 (1 self)
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Abstract. Given a set of objects with profits (any, even negative, numbers) assigned not only to separate objects but also to pairs of them, the unconstrained binary quadratic optimization problem consists in finding a subset of objects for which the overall profit is maximized. In this paper, an iterated tabu search algorithm for solving this problem is proposed. Computational results for problem instances of size up to 7000 variables (objects) are reported and comparisons with other uptodate heuristic methods are provided. Key words: binary quadratic optimization, iterated tabu search, heuristics. 1.
A Hybrid Metaheuristic Approach to Solving the UBQP Problem
 TO APPEAR IN EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2010)
, 2010
"... This paper presents a hybrid metaheuristic approach (HMA) for solving the Unconstrained Binary Quadratic Programming (UBQP) problem. By incorporating a tabu search procedure into the framework of evolutionary algorithms, the proposed approach exhibits several distinguishing features, including a div ..."
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Cited by 9 (4 self)
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This paper presents a hybrid metaheuristic approach (HMA) for solving the Unconstrained Binary Quadratic Programming (UBQP) problem. By incorporating a tabu search procedure into the framework of evolutionary algorithms, the proposed approach exhibits several distinguishing features, including a diversificationbased combination operator and a distanceandquality based replacement criterion for pool updating. The proposed algorithm is able to easily obtain the bestknown solutions for 31 large random instances up to 7000 variables (which no previous algorithm has done) and find new best solutions for 3 of 9 instances derived from the set partitioning problem, demonstrating the efficacy of our proposed algorithm in terms of both solution quality and computational efficiency. Furthermore, some key elements and properties of the HMA algorithm are also analyzed.
Iterated kopt local search for the maximum clique problem
 In Proc. of EvoCOP07
, 2007
"... Abstract. This paper presents a simple iterated local search metaheuristic incorporating a kopt local search (KLS), called Iterated KLS (IKLS for short), for solving the maximum clique problem (MCP). IKLS consists of three components: LocalSearch at which KLS is used, a Kick called LECKick that es ..."
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Cited by 6 (1 self)
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Abstract. This paper presents a simple iterated local search metaheuristic incorporating a kopt local search (KLS), called Iterated KLS (IKLS for short), for solving the maximum clique problem (MCP). IKLS consists of three components: LocalSearch at which KLS is used, a Kick called LECKick that escapes from local optima, and Restart that occasionally diversifies the search by moving to other points in the search space. IKLS is evaluated on DIMACS benchmark graphs. The results showed that IKLS is an effective algorithm for the MCP through comparisons with multistart KLS and stateoftheart metaheuristics. 1
Path Relinking for Unconstrained Binary Quadratic Programming
, 2012
"... This paper presents two path relinking algorithms to solve the unconstrained binary quadratic programming (UBQP) problem. One is based on a greedy strategy to generate the relinking path from the initial solution to the guiding solution and the other operates in a random way. We show extensive compu ..."
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Cited by 6 (1 self)
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This paper presents two path relinking algorithms to solve the unconstrained binary quadratic programming (UBQP) problem. One is based on a greedy strategy to generate the relinking path from the initial solution to the guiding solution and the other operates in a random way. We show extensive computational results on five sets of benchmarks, including 31 large random UBQP instances and 103 structured instances derived from the MaxCut problem. Comparisons with several stateoftheart algorithms demonstrate the efficacy of our proposed algorithms in terms of both solution quality and computational efficiency. It is noteworthy that both algorithms are able to improve the previous best known results for almost 40 percent of the 103 MaxCut instances.
Neighborhood Combination for Unconstrained Binary Quadratic Problems
"... Abstract. We present an experimental analysis of neighborhood combinations for local search based metaheuristic algorithms, using the Unconstrained Binary Quadratic Programming (UBQP) problem as a case study. The goal of the analysis is to help understand why, when and how some neighborhoods can be ..."
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Cited by 5 (4 self)
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Abstract. We present an experimental analysis of neighborhood combinations for local search based metaheuristic algorithms, using the Unconstrained Binary Quadratic Programming (UBQP) problem as a case study. The goal of the analysis is to help understand why, when and how some neighborhoods can be favorably combined to increase their search power. Our study investigates combined neighborhoods with two types of moves for the UBQP problem within a Tabu Search algorithm to determine which strategies for combining neighborhoods prove most valuable.
An Iterated Local Search Approach for Minimum SumOfSquares Clustering
"... Abstract. Since minimum sumofsquares clustering (MSSC) is an NPhard combinatorial optimization problem, applying techniques from global optimization appears to be promising for reliably clustering numerical data. In this paper, concepts of combinatorial heuristic optimization are considered for a ..."
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Cited by 4 (0 self)
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Abstract. Since minimum sumofsquares clustering (MSSC) is an NPhard combinatorial optimization problem, applying techniques from global optimization appears to be promising for reliably clustering numerical data. In this paper, concepts of combinatorial heuristic optimization are considered for approaching the MSSC: An iterated local search (ILS) approach is proposed which is capable of finding (near)optimum solutions very quickly. On gene expression data resulting from biological microarray experiments, it is shown that ILS outperforms multi–start kmeans as well as three other clustering heuristics combined with kmeans. 1
A Hybrid Metaheuristic for Multiobjective Unconstrained Binary Quadratic Programming
, 2013
"... The conventional Unconstrained Binary Quadratic Programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the singleobjective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimi ..."
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Cited by 3 (1 self)
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The conventional Unconstrained Binary Quadratic Programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the singleobjective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a stateoftheart singleobjective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on largesize mUBQP instances with two and three objectives.
Greedy, Genetic, and Greedy Genetic Algorithms for the Quadratic Knapsack Problem
 GECCO'05
, 2005
"... Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem extends the familiar Knapsack Problem by assigning values not only to individual objects but also to pairs of objects. In t ..."
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Cited by 3 (0 self)
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Augmenting an evolutionary algorithm with knowledge of its target problem can yield a more effective algorithm, as this presentation illustrates. The Quadratic Knapsack Problem extends the familiar Knapsack Problem by assigning values not only to individual objects but also to pairs of objects. In these problems, an object’s value density is the sum of the values associated with it divided by its weight. Two greedy heuristics for the quadratic problem examine objects for inclusion in the knapsack in descending order of their value densities. Two genetic algorithms encode candidate selections of objects as binary strings and generate only strings whose selections of objects have total weight no more than the knapsack’s capacity. One GA is naive; its operators apply no information about the values associated with objects. The second extends the naive GA with greedy techniques from the nonevolutionary heuristics. Its operators examine objects for inclusion in the knapsack in orders determined by tournaments based on objects’ value densities. All four algorithms are tested on twenty problem instances whose optimum knapsack values are known. The greedy heuristics do well, as does the naive GA, but the greedy GA exhibits the best performance. In repeated trials on the test instances, it identifies optimum solutions more than nine times out of every ten.
GRASS: a generic algorithm for scaffolding nextgeneration sequencing assemblies
"... Motivation: The increasing availability of secondgeneration highthroughput sequencing (HTS) technologies has sparked a growing interest in de novo genome sequencing. This in turn has fueled the need for reliable means of obtaining highquality draft genomes from shortread sequencing data. The mil ..."
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Cited by 2 (0 self)
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Motivation: The increasing availability of secondgeneration highthroughput sequencing (HTS) technologies has sparked a growing interest in de novo genome sequencing. This in turn has fueled the need for reliable means of obtaining highquality draft genomes from shortread sequencing data. The millions of reads usually involved in HTS experiments are first assembled into longer fragments called contigs, which are then scaffolded, i.e. ordered and oriented using additional information, to produce even longer sequences called scaffolds. Most existing scaffolders of HTS genome assemblies are not suited for using information other than paired reads to perform scaffolding. They use this limited information to construct scaffolds, often preferring scaffold length over accuracy, when faced with the tradeoff. Results: We present GRASS (GeneRic ASsembly Scaffolder)—