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Education and Innovation in Embedded Systems Design

USI Università della Svizzera italiana, USI Faculty of Informatics, Advanced Learning and Research Institute USI Università della Svizzera italiana USI Faculty of Informatics USI Advanced Learning and Research Institute
TitleSolving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach
Publication TypeJournal Article
Year of Publication2017
AuthorsGee, S. Bong, K. Chen Tan, and C. Alippi
JournalIEEE Transactions on Cybernetics
Volume47
Start Page4223
Issue12
Pagination4223 - 4234
Date Published11/2016
Abstract

Evolutionary multiobjective optimization in dynamic environments is a challenging task, as it requires the optimization algorithm converging to a time-variant Pareto optimal front. This paper proposes a dynamic multiobjective optimization algorithm which utilizes an inverse model set to guide the search towards promising decision regions. In order to reduce the number of fitness evaluations for change detection purpose, a two stage change detection test is proposed which uses the inverse model set to check potential changes in the objective function landscape. Both static and dynamic multiobjective benchmark optimization problems have been considered to evaluate the performance of the proposed algorithm. Experimental results show that the improvement in optimization performance is achievable when the proposed inverse model set is adopted.

DOI10.1109/TCYB.2016.2602561