Higher and lower-level knowledge discovery from Pareto-optimal sets

DOI10.1007/s10898-012-0026-xzbMath1315.90041OpenAlexW2067444433MaRDI QIDQ377755

Sunith Bandaru, Kalyanmoy Deb

Publication date: 7 November 2013

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-012-0026-x

zbMATH Keywords

multi-objective optimizationparameter variationsknowledge discoverydesign principlesinnovizationpreferred solutions

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Multi-objective and goal programming (90C29)

Related Items

Generalized higher-level automated innovization with application to inventory management ⋮ A user-guided innovization -based evolutionary algorithm framework for practical multi-objective optimization problems

Cites Work

• A variational approach to define robustness for parametric multiobjective optimization problems
• A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems
• An interactive solution approach for a bi-objective semi-desirable location problem
• A hybrid method for solving multi-objective global optimization problems