Evolutionary multi-level robust solution search for noisy multi-objective optimisation problems with different noise levels. (2016)

• Record Type: Journal Article
• Authors: Sato, Hiroyuki; Hashimoto, Tomohisa
• Is Part Of: International journal of automation and logistics. Volume 2:Number 1/2(2016)
• Page Start: 4
• Page End: 25
• Publication Date: 2016
• Subjects: noise levels -- multi-objective optimisation -- evolutionary algorithms -- multilevel robust solutions -- preference-based search; Automation -- Periodicals; Logistics -- Periodicals; Logistics -- Computer programs -- Periodicals; 658.514
• Languages: English
• ISSNs: 2049-6745

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.