This paper describes a robust version to the TEAM 22 benchmark optimization problem and presents the methodology WCSA (worst case scenario approximation) to solve this problem and other similar cases. The robust multi-objective TEAM 22 model was built from its classical configuration by assuming the imprecisions in the design space. General and specific robust optimization formulas were developed to elaborate WCSA approach. WCSA adds an uncertainty parameter in the objective and constraint functions to perform the role of the system's imprecisions. A multi-objective genetic algorithm approach was chosen to deal with the robust formulation and to find out the set of robust minimizers that matches with the problem requirements. The behavior of the robust Pareto front is also examined.

This paper investigates the use of interval analysis to solve the problem of maneuvering target tracking, using range-only measures collected by a multistatic radar. The problem consists in one transmitter, and some receivers working together as a multistatic radar. The radar process is plagued by several uncertainty sources that affect directly the receivers' measures. As a result, target tracking can be both imprecise and unreliable. This study presents the tracking using an interval-based approach that computes the set of all feasible configurations for the target which are consistent with the measures. The algorithm is compared to a conventional tracking method: particle filtering.

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.