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Clusters of Nondominated Solutions in Multiobjective Combinatorial Optimization
 MOPGP’06: 7TH INT. CONF. ON MULTIOBJECTIVE PROGRAMMING AND GOAL PROGRAMMING
, 2006
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ParameterLess CoClustering for StarStructured Heterogeneous Data
, 2013
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Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
, 2009
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Meeting Deadlines Cheaply
, 2010
"... We develop a computational framework for solving the problem of finding the cheapest configuration (in terms of the number of processors and their respective speeds) of a multiprocessor architecture on which a task graph can be scheduled within a given deadline. We then extend the problem in two ort ..."
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We develop a computational framework for solving the problem of finding the cheapest configuration (in terms of the number of processors and their respective speeds) of a multiprocessor architecture on which a task graph can be scheduled within a given deadline. We then extend the problem in two orthogonal directions: taking communication volume into account and considering the case where a stream of instances of the task graph arrives periodically.
A sequential design for approximating the pareto front . . .
, 2009
"... This thesis proposes a methodology for the simultaneous optimization of multiple goal functions via computer experiments. Some technical challenges associated with the black box multiobjective problem (MOP) can be enumerated as follows: the presence of conflicting goals imply that more optimization ..."
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This thesis proposes a methodology for the simultaneous optimization of multiple goal functions via computer experiments. Some technical challenges associated with the black box multiobjective problem (MOP) can be enumerated as follows: the presence of conflicting goals imply that more optimization effort is invested to find a good range of solutions that are simultaneously optimal against these competing criteria; the highly nonlinear mapping between the inputs in the design space and the goal functions in objective space may complicate the solution process; and in common with global optimization, the runtime costs of simulation severely limit the number of evaluations that can be made. In view of these, the aim is to compute efficiently and identify a set of good solutions that collectively provide an even coverage of the Pareto front, the set of optimal solutions for a given MOP. The members of the Pareto front comprise the set of compromise solutions from which a decision maker chooses a final design that
Metaheuristics and cooperative approaches for the Biobjective Ring Star Problem, in "Computers
 Operations Research
"... This paper presents and investigates different approaches to solve a new biobjective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost t ..."
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This paper presents and investigates different approaches to solve a new biobjective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost that is related to the length of the cycle. The second one is the minimization of an assignment cost from nonvisited nodes to visited ones. In spite of its obvious biobjective formulation, this problem has always been investigated in a singleobjective way. To tackle the biobjective ring star problem, we first investigate different standalone search methods. Then, we propose two cooperative strategies that combine two multiobjective metaheuristics: an elitist evolutionary algorithm and a populationbased local search. We apply these new hybrid approaches to wellknown benchmark test instances and demonstrate their effectiveness in comparison to nonhybrid algorithms and to stateoftheart methods.
On Universal Search Strategies for MultiCriteria Optimization ⋆
"... Abstract. We develop a stochastic local search algorithm for finding Pareto points for multicriteria optimization problems. The algorithm alternates between different singlecriterium optimization problems characterized by weight vectors. The policy for switching between different weights is an ada ..."
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Abstract. We develop a stochastic local search algorithm for finding Pareto points for multicriteria optimization problems. The algorithm alternates between different singlecriterium optimization problems characterized by weight vectors. The policy for switching between different weights is an adaptation of the universal restart strategy defined by [LSZ93] in the context of Las Vegas algorithms. We demonstrate the effectiveness of our algorithm on multicriteria quadratic assignment problem benchmarks and prove some of its theoretical properties. 1
Automatic configuration of . . .  A Case Study on Multiobjective Flowshop Scheduling
, 2011
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GPUbased Approaches for Multiobjective Local Search Algorithms. A Case Study: the Flowshop Scheduling Problem
"... Abstract. Multiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large probl ..."
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Abstract. Multiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large problem instances are to be solved. As a result, the use of GPU computing has been recently revealed as an efficient way to accelerate the search process. This paper presents a new methodology to design and implement efficiently GPUbased multiobjective local search algorithms. The experimental results show that the approach is promising especially for large problem instances.
GPUbased Approaches for Multiobjective Local Search Algorithms. A Case Study: the Flowshop Scheduling Problem
, 2011
"... Abstract. Multiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large proble ..."
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Abstract. Multiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large problem instances are to be solved. As a result, the use of GPU computing has been recently revealed as an efficient way to accelerate the search process. This paper presents a new methodology to design and implement efficiently GPUbased multiobjective local search algorithms. The experimental results show that the approach is promising especially for large problem instances. 1