Deep Statistical Comparison Applied on Quality Indicators to Compare Multi-objective Stochastic Optimization Algorithms
Authors
T.Eftimov, P. Korošec, B. Koroušić Seljak
Publication
Springer LNCS 10710, 2017, 76-87
Abstract
In this paper, a study of how to compare the performance of multi-objective stochastic optimization algorithms using quality indicators and Deep Statistical Comparison (DSC) approach is presented. DSC is a recently proposed approach for statistical comparison of meta-heuristic stochastic optimization algorithms over single-objective problems. The main contribution of DSC is the ranking scheme that is based on the whole distribution, instead of using only one statistic such as average or median. Experimental results performed by using 6 multi-objective stochastic optimization algorithms on 16 test problems show that the DSC gives more robust results compared to some standard statistical approaches that are recommended for a comparison of multi-objective stochastic optimization algorithms according to some quality indicator.
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