Quarterly (spring, summer, fall, winter)
176 pp. per issue
7 x 10
ISSN
1063-6560
E-ISSN
1530-9304
2014 Impact factor:
2.37

Evolutionary Computation

Spring 2019, Vol. 27, No. 1, Pages 147-171
(doi: 10.1162/evco_a_00240)
© 2018 Massachusetts Institute of Technology
Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems
Article PDF (1017.75 KB)
Abstract
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.