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Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm.

Zăvoianu, Alexandru-Ciprian; Saminger-Platz, Susanne; Lughofer, Edwin; Amrhein, Wolfgang

Authors

Susanne Saminger-Platz

Edwin Lughofer

Wolfgang Amrhein



Contributors

Hernán Aguirre
Editor

Abstract

We describe two enhancements that significantly improve the rapid convergence behavior of DECM02 - 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.

Start Date Jul 15, 2018
Publication Date Jul 2, 2018
Publisher Association for Computing Machinery
Pages 793-800
ISBN 9781450356183
Institution Citation ZǍVOIANU, A.-C., SAMINGER-PLATZ, S., LUGHOFER, E. and AMRHEIN, W. 2018. Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm. In Aguirre, H. (ed.) Proceedings of the 2018 Genetic and evolutionary computation conference (GECCO'18), 15-19 July 2018, Kyoto, Japan. New York: Association for Computing Machinery [online], pages 793-800. Available from: https://doi.org/10.1145/3205455.3205549
DOI https://doi.org/10.1145/3205455.3205549
Keywords Coevolution; Sub-population dynamics; Differential evolution; Run-time performance assessment; Multi-objective optimisation

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