Parallel global optimisation meta-heuristics using an asynchronous island-model

We propose an asynchronous island-model algorithm distribution framework and test the popular Differential Evolution algorithm performance when a few processors are available. We confirm that the island-model introduces the possibility of creating new algorithms consistently going beyond the performances of parallel Differential Evolution multi starts. Moreover, we suggest that using heterogeneous strategies along different islands consistently reaches the reliability and performance of the best of the strategies involved, thus alleviating the problem of algorithm selection. We base our conclusions on experiments performed on high dimensional standard test problems (Rosenbrock 100, Rastrigin 300, Lennard Jones 10 atoms), but also, remarkably, on complex spacecraft interplanetary trajectory optimisation test problems (Messenger, Cassini, GTOC1). Spacecraft trajectory global optimisation problems have been recently proposed as hard benchmark problems for continuous global optimisation. High computational resources needed to tackle these type of problems make them an ideal playground for the development and testing of high performance computing algorithms based on multiple processor availability.