Name
Abstract | ||
|---|---|---|
This paper 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 plugin-based architecture of the framework minimizes the end user effort required to incorporate their own problems and evolutionary algorithms, and facilitates tool maintenance. A wide variety of configuration options can be specified to adapt the software behavior to many different parallel models. An innovation of the framework is that it provides a self-adaptive parallel model that is based on the cooperation of a set of evolutionary algorithms. The aim of the new model is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. The model proposed is a hybrid algorithm that combines a parallel is land-based scheme with a hyperheuristic approach. The model grants more computational resources to those algorithms that show a more promising behavior. The flexibility and efficiency of the framework were tested and demonstrated by configuring standard and self-adaptive models for test problems and real-world applications. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S0218213009000275 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | DocType | Volume |
Multi-objective optimization, evolutionary algorithms, parallel optimization, island-based models, plugin-based frameworks | Journal | 18 |
Issue | ISSN | Citations |
4 | 0218-2130 | 27 |
PageRank | References | Authors |
1.02 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Coromoto León | 1 | 231 | 25.71 |
| Gara Miranda | 2 | 188 | 18.16 |
| Carlos Segura | 3 | 216 | 21.44 |