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Title: Evolutionary multi-level robust solution search for noisy multi-objective optimisation problems with different noise levels

Authors: Hiroyuki Sato; Tomohisa Hashimoto

Addresses: Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan ' Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan

Abstract: For noisy multi-objective optimisation problems involving multiple noisy objective functions with different noise levels, this work proposes a multiobjective evolutionary algorithm for multi-level robust solution search (MOEAMRS). MOEA-MRS simultaneously finds multi-level robust solutions with different noise levels for each search direction in the objective space. Furthermore, as an extension of MOEA-MRS, we also propose a MOEA for preference-based multi-level robust solution search (MOEA-pMRS) which focuses the solutions search on a specific noise level to consider the case that the decision maker has a preference for the noise level. The experimental results using noisy DTLZ2 and multi-objective knapsack problems shows that the proposed MOEA-MRS is able to obtain multi-level robust solutions with different noise levels for each search direction in a single run of the algorithm, and the proposed MOEA-pMRS can emphasise the solution search for specific noise levels.

Keywords: noise levels; multi-objective optimisation; evolutionary algorithms; multilevel robust solutions; preference-based search.

DOI: 10.1504/IJAL.2016.074906

International Journal of Automation and Logistics, 2016 Vol.2 No.1/2, pp.4 - 25

Received: 02 Feb 2015
Accepted: 04 Apr 2015

Published online: 24 Feb 2016 *

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