eprintid: 10068492
rev_number: 27
eprint_status: archive
userid: 608
dir: disk0/10/06/84/92
datestamp: 2019-02-21 15:06:11
lastmod: 2021-12-20 23:42:26
status_changed: 2019-02-21 15:06:11
type: proceedings_section
metadata_visibility: show
creators_name: Blot, A
creators_name: Hoos, HH
creators_name: Kessaci, M-E
creators_name: Jourdan, L
title: Automatic Configuration of Bi-objective Optimisation Algorithms: Impact of Correlation between Objectives
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Automatic algorithm configuration
,
Multi objective optimisation
,
Combinatorial optimisation
,
Heuristic algorithms
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2018-12-17
date_type: published
publisher: IEEE
official_url: https://ieeexplore.ieee.org/document/8576091
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1629255
doi: 10.1109/ICTA1.2018.00093
lyricists_name: Blot, Aymeric
lyricists_id: ABLOT72
actors_name: Austen, Jennifer
actors_id: JAUST66
actors_role: owner
full_text_status: public
series: Proceedings-International Conference on Tools With Artificial Intelligence
publication: 2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI)
pagerange: 571-578
pages: 8
event_title: 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
event_location: Volos, GREECE
event_dates: 05 November 2018 - 07 November 2018
institution: 30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
issn: 1082-3409
book_title: 30th IEEE International Conference on Tools with Artificial Intelligence Proceedings
citation:        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   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10068492/1/ictai_2018_preprint.pdf