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GRASP with Path Relinking for the SumCut Problem
 International Journal of Combinatorial Optimization Problems and Informatics
"... Abstract. This paper proposes a GRASP algorithm combined with Path Relinking to solve the SumCut minimization problem. In the SumCut problem one is given a graph with n nodes and must label the nodes in a way that each node receives a unique label from the set{1, 2, … , n}, in order to minimize the ..."
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Abstract. This paper proposes a GRASP algorithm combined with Path Relinking to solve the SumCut minimization problem. In the SumCut problem one is given a graph with n nodes and must label the nodes in a way that each node receives a unique label from the set{1, 2, … , n}, in order to minimize the sum cut of the generated solution. The SumCut problem is really important in archeology (in seriation tasks) and in genetics, helping in the Human Genome Project. This problem is equivalent to the Profile problem, because a solution for SumCut is reversal solution for Profile problem. Experimental results show that the GRASP and Path Relinking methods presented outperform in terms of average percentage deviation the results from the State of the Art using shorter CPU time.
Optimal Broadcasting in Metropolitan MANETs Using Multiobjective Scatter Search ⋆
"... Abstract. Mobile Adhoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any preexisting infrastructure. In such scenario, broadcasting becomes an operation of capital importance for the own existence and operation of the network. ..."
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Abstract. Mobile Adhoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any preexisting infrastructure. In such scenario, broadcasting becomes an operation of capital importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. In this paper, we face this multiobjective problem with a stateoftheart multiobjective scatter search algorithm called AbSS (Archivebased Scatter Search) that computes a Pareto front of solutions to empower a human designer with the ability of choosing the preferred configuration for the network. Results are compared against those obtained with the previous proposal used for solving the problem, a cellular multiobjective genetic algorithm (cMOGA). We conclude that AbSS outperforms cMOGA with respect to three different metrics. 1
SURROGATE SEARCH: A SIMULATION OPTIMIZATION METHODOLOGY FOR LARGESCALE SYSTEMS
, 2006
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Evolutionary Strategies as a Verification and Validation Tool
, 2003
"... This is a methodology proofofconcept paper that describes the use of evolutionary algorithms to improve verification and validation (V&V) of a model simulation. Evolutionary algorithms (EAs) have two characteristics useful for V&V. First, they search a broad range of values in the model’s ..."
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This is a methodology proofofconcept paper that describes the use of evolutionary algorithms to improve verification and validation (V&V) of a model simulation. Evolutionary algorithms (EAs) have two characteristics useful for V&V. First, they search a broad range of values in the model’s parameter space. This allows testing of unusual combinations of parameter values that might not be found by more conventional bounds checking and sensitivity analysis. Second, they exploit with Darwinian ruthlessness any slight evolutionary advantage shown by these combinations, whether or not these combinations of parameter values were intended or anticipated by the designer. This exploitation might, for instance, drive use of a required resource to zero, if the production rules were incorrect or improperly coded. The original contribution to this paper is to identify a new tool for V&V. It is important because it provides a complimentary approach to conducting bounds checking and sensitivity analysis, not previously recognized. There is extensive prior work in verification techniques such as debugging and bounds checking, and in validation techniques such as output review and sensitivity analysis. None of them address the use of evolutionary algorithms for this effort, even when they employ EAs in the optimization of some model parameter set. We believe other researchers can make use of this technique by employing a simple EA in the test phase of their software development.
SCATTER SEARCH WITH MULTIPLE IMPROVEMENT METHODS FOR THE LINEAR ORDERING PROBLEM
"... In this work, the Linear Ordering Problem (LOP) is approached. This is an NPhard problem which has been solved with different metaheuristic algorithms. Particularly, it has been solved with a Scatter Search algorithm that applies the traditional approach which incorporates a single improvement meth ..."
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In this work, the Linear Ordering Problem (LOP) is approached. This is an NPhard problem which has been solved with different metaheuristic algorithms. Particularly, it has been solved with a Scatter Search algorithm that applies the traditional approach which incorporates a single improvement method. In this paper, we propose a Scatter Search algorithm which uses multiple improvement methods to achieve a better balance of intensification and diversification. To validate our approach, a statisticallysupported experimental study of its performance was carried out using the most challenging standard instances. The overall performance of the proposed Scatter Search algorithm was compared with the stateoftheart algorithm solution for LOP. The experimental evidence shows that our algorithm outperforms the best algorithm solution for LOP, improving 2.89 % the number of bestknown solutions obtained, and 71 % the average percentage error. It is worth noticing that it obtains 53 new bestknown solutions for the instances used. We claim that the combination of multiple improvement methods (local searches) can be applied to improve the balance between intensification and diversification in other metaheuristics to solve LOP and problems in other domain.
The AID Method for Global Optimization
, 2011
"... An Alternating Intensification/Diversification (AID) method is proposed to tackle global optimization problems, focusing here on global function minimization over continuous variables. Our method is a local search procedure that is particularly easy to implement, and can readily be embedded as a sup ..."
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An Alternating Intensification/Diversification (AID) method is proposed to tackle global optimization problems, focusing here on global function minimization over continuous variables. Our method is a local search procedure that is particularly easy to implement, and can readily be embedded as a supporting strategy within more sophisticated methods that make use of populationbased designs. We perform computational tests comparing the AID method to 20 other algorithms, many of them representing a similar or higher level of sophistication, on a total of 28 benchmark functions. The results show that the new approach generally obtains good quality solutions for unconstrained global optimization problems, suggesting the utility of its underlying notions and the potential value of exploiting its multiple avenues for generalization.
NEW ADVANCES AND APPLICATIONS FOR MARRYING SIMULATION AND OPTIMIZATION
"... This tutorial will focus on several new realworld applications that have been developed using an integrated set of ..."
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This tutorial will focus on several new realworld applications that have been developed using an integrated set of
nonlinear
, 2007
"... Simple metaheuristics using the simplex algorithm for nonlinear programming ..."
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Simple metaheuristics using the simplex algorithm for nonlinear programming
Electronic Letters on Computer Vision and Image Analysis 5(3):6883, 2005 Combining Particle Filter and Populationbased Metaheuristics for Visual Articulated Motion Tracking
, 2004
"... Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Modelbased tracking allows determining object pose in an effortles ..."
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Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Modelbased tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional statespace and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called dynamic optimization problems. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two populationbased metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show the proposed algorithms increase the performance of standard particle filters.