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Automatic Configuration of Bi-objective Optimisation Algorithms: Impact of Correlation between Objectives

Blot, A; Hoos, HH; Kessaci, M-E; Jourdan, L; (2018) Automatic Configuration of Bi-objective Optimisation Algorithms: Impact of Correlation between Objectives. In: 30th IEEE International Conference on Tools with Artificial Intelligence Proceedings. (pp. pp. 571-578). IEEE Green open access

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Abstract

Multi-objective optimisation algorithms expose various parameters that have to be tuned in order to be efficient. Moreover, in multi-objective optimisation, the correlation between objective functions is known to affect search space structure and algorithm performance. Considering the recent success of automatic algorithm configuration (AAC) techniques for the design of multi-objective optimisation algorithms, this raises two interesting questions: what is the impact of correlation between optimisation objectives on (1) the efficacy of different AAC approaches and (2) on the optimised algorithm designs obtained from these automated approaches? In this work, we study these questions for multi-objective local search algorithms (MOLS) for three well-known bi-objective permutation problems, using two single-objective AAC approaches and one multi-objective approach. Our empirical results clearly show that overall, multi-objective AAC is the most effective approach for the automatic configuration of the highly parametric MOLS framework, and that there is no systematic impact of the degree of correlation on the relative performance of the three AAC approaches. We also find that the best-performing configurations differ, depending on the correlation between objectives and the size of the problem instances to be solved, providing further evidence for the usefulness of automatic configuration of multi-objective optimisation algorithms.

Type: Proceedings paper
Title: Automatic Configuration of Bi-objective Optimisation Algorithms: Impact of Correlation between Objectives
Event: 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
Location: Volos, GREECE
Dates: 05 November 2018 - 07 November 2018
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICTA1.2018.00093
Publisher version: https://ieeexplore.ieee.org/document/8576091
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Automatic algorithm configuration , Multi objective optimisation , Combinatorial optimisation , Heuristic algorithms
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10068492
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