Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver.

Zavoianu, Alexandru-Ciprian; Saminger-Platz, Susanne; Amrhein, Wolfgang

Abstract

We describe and compare two steady state asynchronous parallelization variants for DECMO2++, a recently proposed multi-objective coevolutionary solver that generally displays a robust run-time convergence behavior. The two asynchronous variants were designed as trade-offs that maintain only two of the three important synchronized interactions / constraints that underpin the (generation-based) DECMO2++ coevolutionary model. A thorough performance evaluation on a test set that aggregates 31 standard benchmark problems shows that while both parallelization options are able to generally preserve the competitive convergence behavior of the baseline coevolutionary solver, the better parallelization choice is to prioritize accurate run-time search adaptation decisions over the ability to perform equidistant fitness sharing.

Citation

ZAVOIANU, A.-C., SAMINGER-PLATZ, S. and AMRHEIN, W. 2019. Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, New Zealand. Piscataway: IEEE [online], article number 8790133, pages 3078-3085. Available from: https://doi.org/10.1109/CEC.2019.8790133

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2019 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2019)
• Start Date: Jun 10, 2019
• End Date: Jun 13, 2019
• Acceptance Date: Apr 5, 2019
• Online Publication Date: Aug 8, 2019
• Publication Date: Aug 8, 2019
• Deposit Date: Jul 9, 2019
• Publicly Available Date: Jul 9, 2019
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 3078-3085
• DOI: https://doi.org/10.1109/CEC.2019.8790133
• Keywords: Multi-objective optimization; Asynchronous coevolution; Master-slave parallelization; Performance analysis
• Public URL: https://rgu-repository.worktribe.com/output/314979
• Contract Date: Jul 9, 2019