Parallel cooperative meta-heuristics on the computational grid.: A case study: the bi-objective Flow-Shop problem

In this paper, we contribute with the first results on parallel cooperative multi-objective meta-heuristics on computational grids. We particularly focus on the island model and the multi-start model and their cooperation. We propose a checkpointing-based approach to deal with the fault tolerance issue of the island model. Nowadays, existing Dispatcher–Worker grid middlewares are inadequate for the deployment of parallel cooperative applications. Indeed, these need to be extended with a software layer to support the cooperation. Therefore, we propose a Linda-like cooperation model and its implementation on top of XtremWeb. This middleware is then used to develop a parallel meta-heuristic applied to a bi-objective Flow-Shop problem using the two models. The work has been experimented on a multi-domain education network of 321 heterogeneous Linux PCs. The preliminary results, obtained after more than 10 days, demonstrate that the use of grid computing allows to fully exploit effectively different parallel models and their combination for solving large-size problem instances. An improvement of the effectiveness by over 60% is realized compared to serial meta-heuristic.