2022 • In Mernik, Marjan; Črepinšek, Matej (Eds.) Bioinspired Optimization Methods and Their Applications - 10th International Conference, BIOMA 2022, Proceedings
[en] Parallel Surrogate-Assisted Evolutionary Algorithms (P-SAEAs) are based on surrogate-informed reproduction operators to propose new candidates to solve computationally expensive optimization problems. Differently, Parallel Surrogate-Driven Algorithms (P-SDAs) rely on the optimization of a surrogate-informed metric of promisingness to acquire new solutions. The former are promoted to deal with moderately computationally expensive problems while the latter are put forward on very costly problems. This paper investigates the design of hybrid strategies combining the acquisition processes of both P-SAEAs and P-SDAs to retain the best of both categories of methods. The objective is to reach robustness with respect to the computational budgets and parallel scalability.
Disciplines :
Computer science
Author, co-author :
Briffoteaux, Guillaume ; Université de Mons - UMONS > Faculté Polytechnique > Service de Mathématique et Recherche opérationnelle ; University of Lille, Inria, UMR 9189 - CRIStAL, Lille, France
Melab, Nouredine; University of Lille, Inria, UMR 9189 - CRIStAL, Lille, France
Mezmaz, Mohand; Mathematics and Operational Research Department, University of Mons, Mons, Belgium
We thank Romain Ragonnet and the Department of Public Health and Preventive Medicine at Monash University in Melbourne, Australia, for helping us to set the AuTuMN simulator. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www. grid5000.fr).
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