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Grid’5000: A large scale and highly reconfigurable grid experimental testbed
 In GRID ’05: Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
, 2005
"... Large scale distributed systems such as Grids are difficult to study from theoretical models and simulators only. Most Grids deployed at large scale are production platforms that are inappropriate research tools because of their limited reconfiguration, control and monitoring capabilities. In this ..."
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Cited by 140 (20 self)
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Large scale distributed systems such as Grids are difficult to study from theoretical models and simulators only. Most Grids deployed at large scale are production platforms that are inappropriate research tools because of their limited reconfiguration, control and monitoring capabilities. In this paper, we present Grid’5000, a 5000 CPU nationwide infrastructure for research in Grid computing. Grid’5000 is designed to provide a scientific tool for computer scientists similar to the largescale instruments used by physicists, astronomers, and biologists. We describe the motivations, design considerations, architecture, control, and monitoring infrastructure of this experimental platform. We present configuration examples and performance results for the reconfiguration subsystem.
Iterated local search vs. hyperheuristics: Towards generalpurpose search algorithms
 In IEEE Congress on Evolutionary Computation (CEC 2010
, 2010
"... Abstract — An important challenge within hyperheuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across different problem domains. This article conducts an empirical study involving three different domains in combin ..."
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Cited by 20 (8 self)
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Abstract — An important challenge within hyperheuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across different problem domains. This article conducts an empirical study involving three different domains in combinatorial optimisation: bin packing, permutation flow shop and personnel scheduling. Using a common software interface (HyFlex), the same algorithms (highlevel strategies or hyperheuristics) can be readily run on all of them. The study is intended as a proof of concept of the proposed interface and domain modules, as a benchmark for testing the generalisation abilities of heuristic search algorithms. Several algorithms and variants from the literature were implemented and tested. From them, the implementation of iterated local search produced the best overall performance. Interestingly, this is one of the most conceptually simple competing algorithms, its advantage as a robust algorithm is probably due to two factors: (i) the simple yet powerful exploration/exploitation balance achieved by systematically combining a perturbation followed by local search; and (ii) its parameterless nature. We believe that the challenge is still open for the design of robust algorithms that can learn and adapt to the available lowlevel heuristics, and thus select and apply them accordingly. I.
Iterated Local Search: Framework and Applications
"... The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also ..."
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Cited by 18 (1 self)
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The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also
A Discrete Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem
"... In this paper, a novel discrete differential evolution (DDE) algorithm is presented to solve the permutation flowhop scheduling problem with the makespan criterion. The DDE algorithm is simple in nature such that it first mutates a target population to produce the mutant population. Then the target ..."
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Cited by 17 (1 self)
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In this paper, a novel discrete differential evolution (DDE) algorithm is presented to solve the permutation flowhop scheduling problem with the makespan criterion. The DDE algorithm is simple in nature such that it first mutates a target population to produce the mutant population. Then the target population is recombined with the mutant population in order to generate a trial population. Finally, a selection operator is applied to both target and trial populations to determine who will survive for the next generation based on fitness evaluations. As a mutation operator in the discrete differential evolution algorithm, a destruction and construction procedure is employed to generate the mutant population. We propose a referenced local search, which is embedded in the discrete differential evolution algorithm to further improve the solution quality. Computational results show that the proposed DDE algorithm with the referenced local search is very competitive to the iterated greedy algorithm which is one of the best performing algorithms for the permutation flowshop scheduling problem in the literature.
A discrete particle swarm optimization algorithm for the generalized traveling salesman problem
 GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007, 158–167. Conclusion 15
"... Dividing the set of nodes into clusters in the wellknown traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented t ..."
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Cited by 16 (0 self)
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Dividing the set of nodes into clusters in the wellknown traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speedup methods for greedy node insertions are presented. The discrete particle swarm optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve the generalized traveling salesman problem.
A gridenabled branch and bound algorithm for solving challenging combinatorial optimization problems
, 2006
"... Solving optimally large instances of combinatorial optimization problems requires a huge amount of computational resources. In this paper, we propose an adaptation of the parallel Branch and Bound algorithm for computational grids. Such gridification is based on new ways to efficiently deal with som ..."
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Cited by 13 (8 self)
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Solving optimally large instances of combinatorial optimization problems requires a huge amount of computational resources. In this paper, we propose an adaptation of the parallel Branch and Bound algorithm for computational grids. Such gridification is based on new ways to efficiently deal with some crucial issues, mainly dynamic adaptive load balancing, fault tolerance, global information sharing and termination detection of the algorithm. A new efficient coding of the work units (search subtrees) distributed during the exploration of the search tree is proposed to optimize the involved communications. The algorithm has been implemented following a large scale idle time stealing paradigm (FarmerWorker). It has been experimented on a FlowShop problem instance ( ) that has never been optimally solved. The new algorithm allowed to realize a success story as the optimal solution has been found with proof of optimality, within days using about processors belonging to Nationwide distinct clusters (administration domains). During the resolution, the worker processors were exploited with an average of while the farmer processor was exploited only of the time. These two rates are good indicators on the efficiency of the proposed approach and its scalability.
New High Performing Heuristics for Minimizing Makespan in Permutation Flowshops
, 2006
"... The well known NEH heuristic from Nawaz, Enscore and Ham proposed in 1983 has been recognized as the highest performing method for the permutation flowshop scheduling problem under the makespan minimization criterion. This performance lead is maintained even today when compared against contemporar ..."
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Cited by 9 (1 self)
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The well known NEH heuristic from Nawaz, Enscore and Ham proposed in 1983 has been recognized as the highest performing method for the permutation flowshop scheduling problem under the makespan minimization criterion. This performance lead is maintained even today when compared against contemporary and more complex heuristics as shown in recent studies. In this paper we show five new methods that outperform NEH as supported by careful statistical analyses using the well known instances of Taillard. The proposed methods try to counter the excessive greediness of NEH by carrying out reinsertions of already inserted jobs at some points in the construction of the solution. The five proposed heuristics range from extensions that are slightly slower than NEH in most tested instances to more comprehensive methods based on local search that yield excellent results at the expense of some added computational time. Additionally, NEH has been profusely used in the flowshop scheduling literature as a seed sequence in high performing metaheuristics. We demonstrate that using some of our proposed heuristics as seeds yields better final results in comparison.
On the neutrality of flowshop scheduling fitness landscapes
 IN: PROCEEDINGS OF THE 5TH LEARNING AND INTELLIGENT OPTIMIZATION CONFERENCE
, 2011
"... Solving efficiently complex problems using metaheuristics, and in particular local search algorithms, requires incorporating knowledge about the problem to solve. In this paper, the permutation flowshop problem is studied. It is well known that in such problems, several solutions may have the same ..."
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Cited by 5 (2 self)
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Solving efficiently complex problems using metaheuristics, and in particular local search algorithms, requires incorporating knowledge about the problem to solve. In this paper, the permutation flowshop problem is studied. It is well known that in such problems, several solutions may have the same fitness value. As this neutrality property is an important issue, it should be taken into account during the design of search methods. Then, in the context of the permutation flowshop, a deep landscape analysis focused on the neutrality property is driven and propositions on the way to use this neutrality in order to guide the search efficiently are given.
A TwoPhase Optimization Algorithm For Mastermind
, 2007
"... This paper presents a systematic model, twophase optimization algorithms (TPOA), for Mastermind. TPOA is not only able to efficiently obtain approximate results but also effectively discover results that are getting closer to the optima. This systematic approach could be regarded as a general impro ..."
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Cited by 5 (0 self)
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This paper presents a systematic model, twophase optimization algorithms (TPOA), for Mastermind. TPOA is not only able to efficiently obtain approximate results but also effectively discover results that are getting closer to the optima. This systematic approach could be regarded as a general improver for heuristics. That is, given a constructive heuristic, TPOA has a higher chance to obtain results better than those obtained by the heuristic. Moreover, it sometimes can achieve optimal results that are difficult to find by the given heuristic. Experimental results show that (i) TPOA with parameter setting (k, d) 5 (1, 1) is able to obtain the optimal result for the game in the worst case, where k is the branching factor and d is the exploration depth of the search space. (ii) Using a simple heuristic, TPOA achieves the optimal result for the game in the expected case with (k, d) 5 (180, 2). This is the first approximate approach to achieve the optimal result in the expected case.