Parallel tabu search message-passing synchronous strategies for task scheduling under precedence constraints

Porto, Stella C. S.; Ribeiro, Celso C.

doi:10.1007/bf00127078

Cited by 40 publications

(19 citation statements)

References 12 publications

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“…An example of such an approach for TS is presented in [112]. The neighborhood is partitioned in equal size partitions depending on the number of workers, which is equal to the number of processors of the parallel machine.…”

Section: Implementing Parallel Hybrid Metaheuristicsmentioning

confidence: 99%

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Parallel Hybrid Metaheuristics

Cotta

Talbi

Alba

2005

Parallel Metaheuristics

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Finding optimal solutions is in general computationally intractable for many combinatorial optimization problems, e.g., those known as NP-hard [54]. The classical approach for dealing with this fact was the use of approximation algorithms, i.e., relaxing the goal from finding the optimal solution to obtaining solutions within some bounded distance from the former [61]. Unfortunately, it turns out that attainable bounds in practice (that is, at a tenable computational cost) are in general too far from the optimum to be useful in many problems. The days in which researchers struggled to slightly tighten worst-case bounds that were anyway far from practical, or in which finding a PTAS (let alone a FPTAS) for a certain problem was considered a whole success are thus swiftly coming to an end. Indeed, two new alternative lines of attack are being currently used to treat these difficulties. On one hand, a new corpus of theory is being built around the notion of fixed-parameter tractability that emanates from the field of parameterized complexity [40][41]. On the other hand, metaheuristics approaches are being increasingly used nowadays. Quoting [27], the philosophy of these latter techniques is "try to obtain probably optimal solutions to your problem, for provably good solutions are overwhelmingly hard to obtain". See also [27][97] for some prospects on the intersection of both fields (parameterized complexity and metaheuristics).Focusing on the latter techniques, metaheuristics approaches can be broadly categorized into two major classes: single-solution search algorithms (also known as trajectory-based or local-search based algorithms), and multiple-solution search algorithms (also-known as population-based or -arguably stretching the term-evolutionary algorithms). Examples of the former class are descent local search (LS) Over the years, interest in metaheuristics has risen considerably among researchers in combinatorial optimization. The flexibility of these techniques makes them prime candidates for tackling both new problems and variants of exiting problems. This fact, i[

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Section: Implementing Parallel Hybrid Metaheuristicsmentioning

confidence: 99%

“…• Dynamic: to improve the performance of parallel static heuristics, dynamic load balancing must be introduced [112][12]. This class represents heuristics for which the number of tasks is fixed at compilation time, but the location of work (tasks, data) is determined and/or changed at run-time.…”

Section: Implementing Parallel Hybrid Metaheuristicsmentioning

confidence: 99%

Parallel Hybrid Metaheuristics

Cotta

Talbi

Alba

2005

Parallel Metaheuristics

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“…An example of such an approach for parallel tabu search is presented in [29]. The neighborhood is partioned into equal size partitions depending on the number of workers, which is equal to the number of processors of the parallel machine.…”

Section: Parallel Hybrid Implementationsmentioning

confidence: 99%

“…• semi-adaptive: to improve the performance of parallel non-adaptive algorithms, dynamic load balancing must be introduced [29][3]. This class represents algorithms for which the number of tasks is fixed at compile-time, but the location of work (tasks, data) is determined and/or changed at run-time.…”

Section: Parallel Hybrid Implementationsmentioning

confidence: 99%

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Towards hybrid evolutionary algorithms

Preux

Talbi

1999

Int Trans Operational Res

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Metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. However, the choice of a particular algorithm to optimize a certain problem is still mainly driven by some sort of devotion of its author to a certain technique rather than by a rationalistic choice driven by reason. Hybrid algorithms have shown their ability to provide local optima of high quality. Hybridization of algorithms is still in its infancy: certain combinations of algorithms have experimentally shown their performance, though the reasons of their success is not always really clear. In order to add some rational to these issues, we study the structure of search spaces and attempt to relate it to the performance of algorithms. We wish to explain the behavior of search algorithms with this knowledge and provide guidelines in the design of hybrid algorithms. This paper briefly reviews the current knowledge we have on search spaces of combinatorial optimization problems. Then, we discuss hybridization and present a general classification of the way hybridization can be conducted in the light of our knowledge of the structure of search spaces.

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Parallel Strategies for Meta-Heuristics

Crainic

Toulouse

International Series in Operations Research &Amp; Management Science

111

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Parallel tabu search message-passing synchronous strategies for task scheduling under precedence constraints

Cited by 40 publications

References 12 publications

Parallel Hybrid Metaheuristics

Parallel Hybrid Metaheuristics

Towards hybrid evolutionary algorithms

Parallel Strategies for Meta-Heuristics

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