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

Anne Auger (1) , Dimo Brockhoff (2) , Nikolaus Hansen (1) , Dejan Tušar (2) , Tea Tušar (2) , Tobias Wagner (3)

1 TAO - Machine Learning and Optimisation
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
3 TU - Technische Universität Dortmund [Dortmund]

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.

Mots clés: Benchmarking; Black-box optimization; Bi-objective optimization

Domaines: Réseau de neurones [cs.NE]; Optimisation et contrôle [math.OC]

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⟩