PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

We present an algorithm for multi-objective optimization of computationally expensive problems. The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one, so that only solutions estimated to be approximately Pareto-optimal are evaluated using the real e...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Cuervo, Santiago, Melgarejo, Miguel, Blanco-Cañon, Angie, Reyes-Fajardo, Laura, Rojas-Galeano, Sergio
Format: Article
Language:English
Subjects:
Algorithms
Evolutionary algorithms
Multiple objective analysis
Optimization
Pareto optimization
Search methods
Online Access:Get full text
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Summary:We present an algorithm for multi-objective optimization of computationally expensive problems. The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one, so that only solutions estimated to be approximately Pareto-optimal are evaluated using the real expensive functions. Aside of the search for solutions, our algorithm also performs a meta-search for optimal surrogate models and navigation strategies for the optimization landscape, therefore adapting the search strategy for solutions to the problem as new information about it is obtained. The competitiveness of our approach is demonstrated by an experimental comparison with one state-of-the-art surrogate-assisted evolutionary algorithm on a set of benchmark problems.
ISSN:2331-8422