Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems

Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorith...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Evolutionary computation Ročník 27; číslo 1; s. 147
Hlavní autoři: Blot, Aymeric, Kessaci, Marie-Éléonore, Jourdan, Laetitia, Hoos, Holger H
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.03.2019
Témata:
travelling salesman problem
permutation flowshop scheduling problem
local search
multi-objective optimisation
Algorithm configuration
ISSN:1530-9304, 1530-9304
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective optimisation problems, the bi-objective permutation flowshop and travelling salesman problems. Our results show that multi-objective algorithms are indeed best configured using a multi-objective configurator.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1530-9304
1530-9304
DOI:10.1162/evco_a_00240