A Parallel Plugin-Based Framework for Multi-objective Optimization

Abstract

This work presents a parallel framework for the solution of multi-objective optimization problems. The framework implements some of the best known multi-objective evolutionary algorithms. The framework architecture makes usage of configuration files to provide a more extensive and simple customization environment than other similar tools. A wide variety of configuration options can be specified to adapt the software behaviour to many different parallel models, including a new adaptive model which dynamically grants more computational resources to the most promising algorithms. The plugin-based architecture of the framework minimizes the final user effort required to incorporate their own problems and evolutionary algorithms, and facilitates the tool maintenance. The flexibility of the approach has been tested by configuring a standard homogeneous island-based model and a self-adaptive model. The computational results obtained for problems with different granularity demonstrate the efficiency of the provided parallel implementation.