Interval Robust Multi-Objective Evolutionary Algorithm

• Published in: 2009 IEEE Congress on Evolutionary Computation pp. 1637 - 1643
• Main Authors: Soares, G.L., Guimaraes, F.G., Maia, C.A., Vasconcelos, J.A., Jaulin, L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2009
• Subjects: Algorithm design and analysis; Constraint optimization; Design optimization; evolutionary algorithms; Evolutionary computation; genetic algorithms; IEEE members; interval analysis; Noise robustness; Pareto analysis; Pareto optimization; robust multi-objective optimization; robust Pareto front; robust test functions; Testing; Uncertainty
• ISBN: 1424429587; 9781424429585
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/CEC.2009.4983138

Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval Robust Multi-Objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multi-objective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.