Article ID: | iaor200922522 |
Country: | United States |
Volume: | 42 |
Issue: | 1 |
Start Page Number: | 105 |
End Page Number: | 139 |
Publication Date: | Jan 2009 |
Journal: | Computational Optimization and Applications |
Authors: | Bui Lam T, Abbass Hussein A, Essam Daryl |
Keywords: | programming: multiple criteria |
When solving real–world optimization problems, evolutionary algorithms often require a large number of fitness evaluations in order to converge to the global optima. Attempts have been made to find techniques to reduce the number of fitness function evaluations. We propose a novel framework in the context of multi–objective optimization where fitness evaluations are distributed by creating a limited number of adaptive spheres spanning the search space. These spheres move towards the global Pareto front as components of a swarm optimization system. We call this process localization. The contribution of the paper is a general framework for distributed evolutionary multi–objective optimization, in which the individuals in each sphere can be controlled by any existing evolutionary multi–objective optimization algorithm in the literature.