6533b857fe1ef96bd12b44b9

RESEARCH PRODUCT

Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms

Karthik SindhyaTinkle ChughYaochu JinJussi HakanenKaisa Miettinen

subject

Optimization problemLinear programmingComputer science0211 other engineering and technologiesEvolutionary algorithmInteractive evolutionary computationpreference information02 engineering and technologyMachine learningcomputer.software_genredecision makingEvolutionary computationSet (abstract data type)vectors0202 electrical engineering electronic engineering information engineeringta113021103 operations researchbusiness.industryta111Approximation algorithmPreferencemultiobjective evolutionary optimization algorithm020201 artificial intelligence & image processingArtificial intelligencebusinessoptimizationcomputer

description

We study how different types of preference information coming from a human decision maker can be utilized in an interactive multiobjective evolutionary optimization algorithm (MOEA). The idea is to convert different types of preference information into a unified format which can then be utilized in an interactive MOEA to guide the search towards the most preferred solution(s). The format chosen here is a set of reference vectors which is used within the interactive version of the reference vector guided evolutionary algorithm (RVEA). The proposed interactive RVEA is then applied to the multiple-disk clutch brake design problem with five objectives to demonstrate the potential of the idea in supporting decision making in optimization problems involving more than three objectives.

yearjournalcountryeditionlanguage
2016-12-012016 IEEE Symposium Series on Computational Intelligence (SSCI)
https://doi.org/10.1109/ssci.2016.7850220