Ciprian Zavoianu, Susanne Saminger-Platz, Edwin Lughofer, Wolfgang Amrhein,
"Two Enhancements for Improving the Convergence Speed of a Robust Multi-Objective Coevolutionary Algorithm"
: GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, July 15?19, 2018, Kyoto, Japan, ACM, New York, NY, USA, Seite(n) 793-800, 7-2018, ISBN: 978-1-4503-5618-3
Two Enhancements for Improving the Convergence Speed of a Robust Multi-Objective Coevolutionary Algorithm
Sprache des Titels:
GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, July 15?19, 2018, Kyoto, Japan
We describe two enhancements that significantly improve the rapid convergence behavior of a previously proposed robust coevolutionary algorithm that integrates three different multi-objective space exploration paradigms: differential evolution, two-tier Pareto-based selection for survival and decomposition-based evolutionary guidance. The first enhancement is a refined active search adaptation
mechanism that relies on run-time sub-population performance indicators to estimate the convergence stage and dynamically adjust and steer certain parts of the coevolutionary process in order to improve its overall efficiency. The second enhancement consists in a directional intensification operator that is applied in the early part of the run during the decomposition-based search phases. This operator
creates new random local linear individuals based on the recent historically successful solution candidates of a given directional decomposition vector. As the two efficiency-related enhancements are complementary, our results show that the resulting coevolutionary algorithm is a highly competitive improvement of the baseline strategy when considering a comprehensive test set aggregated from 25
(standard) benchmark multi-objective optimization problems.