A distributed and hierarchical strategy for autonomic grid-enabled cooperative metaheuristics with applications

摘要

The adoption of the same cluster-based programming strategies for grid applications, although requiring minimal effort from a programmer’s point of view, does not always take advantage of the available computational resources to their fullest extent. This paper investigates the impact of a distributed and hierarchical autonomic strategy on the performance of parallel metaheuristics to solve hard combinatorial optimization problems on grids. Two problems, the mirrored traveling tournament problem and the bounded diameter minimum spanning tree problem, for which high quality sequential heuristics based on the paradigms of the GRASP and Iterated Local Search metaheuristics already exist, are employed as casestudies. The computational results obtained on a grid by the novel autonomic strategy show that outstanding performance improvements over the traditional master–worker parallelization approach can be achieved.