The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite

Abstract

The S-metric-Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) is one of the best-known indicator-based multi-objective optimization algorithms. It employs the S-metric or hypervolume indicator in its (steady-state) selection by deleting in each iteration the solution that has the smallest contribution to the hypervolume indicator. In the SMS-EMOA, the conceptual idea is this hypervolume-based selection. Hence the algorithm can, for example, be combined with several variation operators. Here, we benchmark two versions of SMS-EMOA which employ differential evolution (DE) and simulated binary crossover (SBX) with polynomial mutation (PM) respectively on the newly introduced bi-objective bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. The results un-surprisingly reveal that the choice of the variation operator is crucial for performance with a clear advantage of the DE variant on almost all functions.
Fichier principal
Vignette du fichier
wk0808-auger-SMSEMOAcomp-authorversion.pdf (4.7 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01435456 , version 1 (14-01-2017)

Identifiers

Cite

Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Dejan Tušar, Tea Tušar, et al.. The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite. GECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, CO, United States. pp.1225 - 1232, ⟨10.1145/2908961.2931705⟩. ⟨hal-01435456⟩
721 View
169 Download

Altmetric

Share

Gmail Facebook X LinkedIn More