Enhancing Cluster Geometry Optimization with Island Models

摘要

Island Models are parallel approaches to Evolutionary Algorithms that not only offer the benefits of parallelization but are also regarded as models with an extensively distinct behaviour. This study applies for the first time an Island Model to the optimization of short-ranged Morse clusters, combined with a hybrid steady-state evolutionary algorithm and a local optimization method. Different migration parameters are experimented and the resulting behaviours are extensively analysed. Results are compared to a state-of-the-art sequential approach, showing slight improvements. Differences in behaviour between the Island Model and the sequential approach are comprehensively discussed. This study shows that Island Models are a competitive parallel approach with promising results on cluster geometry optimization problems.