Paper Info
Title
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 family bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. The results unsurprisingly reveal that the choice of the variation operator is crucial for performance with a clear advantage of the DE variant on almost all functions.
Year
DOI
Venue
2016
10.1145/2908961.2931705
GECCO (Companion)
Keywords
Field
DocType
Benchmarking, Black-box optimization, Bi-objective optimization
Test suite,Mathematical optimization,Crossover,Polynomial,Computer science,Algorithm,Differential evolution,Artificial intelligence,Optimization algorithm,Operator (computer programming),Machine learning,Binary number
Conference
Citations 
PageRank 
References 
5
0.45
10
Authors
6
Name
Order
Citations
PageRank
Anne Auger1119877.81
Dimo Brockhoff294853.97
Nikolaus Hansen372351.44
Dejan Tusar4102.34
Tea Tusar518119.91
Tobias Wagner61379.96