Computational methods for solving multi-objective uncertain optimization problems
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2011-05-12
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Universidad EAFIT
Resumen
In recent years, there has been an increasing interest in the multi-objective uncertain optimization, discussed
in the framework of the interval-valued optimization, as a consequence theoretical developments have
achieved significant results as theorems analogous to the conditions of Karush Kunt Tucker, but computational
developments are still incipient. This paper makes an extension of Strength Pareto Evolutionary Algorithm 2
- SPEA2 - and Multi-objective Particle Swarm Optimization - MOPSO -, which ones are traditionally used in
multi-objective optimization, these are modified to the case of multi-objective uncertain optimization, where
the model uses the interval-valued optimization as shown by Wu [?, ?, ?], these new algorithms have arithmetic advantage in the image set of the objective function. At the end, numerical examples are shown where they applied the algorithms implemented.