Online Parallel Portfolio Selection with Heterogeneous Island Model
This work addresses the challenge of efficient algorithm selection in parallel computing for optimization problems, though it appears incremental as it builds on existing island models.
The paper tackled the problem of selecting optimization algorithms in parallel portfolios by introducing a heterogeneous island model where a central planner dynamically reallocates resources based on performance, and the results showed that this approach is more general and universal than homogeneous models across various test scenarios.
We present an online parallel portfolio selection algorithm based on the island model commonly used for parallelization of evolutionary algorithms. In our case each of the islands runs a different optimization algorithm. The distributed computation is managed by a central planner which periodically changes the running methods during the execution of the algorithm -- less successful methods are removed while new instances of more successful methods are added. We compare different types of planners in the heterogeneous island model among themselves and also to the traditional homogeneous model on a wide set of problems. The tests include experiments with different representations of the individuals and different duration of fitness function evaluations. The results show that heterogeneous models are a more general and universal computational tool compared to homogeneous models.