Local models– an approach to distributed multi-objective optimization

Local models– an approach to distributed multi-objective optimization

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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: , ,
Keywords: programming: multiple criteria
Abstract:

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.

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