Active robust optimization - enhancing robustness to uncertain environments

Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution’s parameters such that the solution’s performance is less influenced by negative effects of the uncertain parameters’ variations. This robustness may be viewed as a passive robustness, because once the solution’s parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes on the expense of peak performances. In this study, Active Robust Optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named Active Robust Optimization Problem is formulated. The problem amalgamates robust optimization with dynamic optimization in order to evaluate the performance of a candidate solution, while considering possible environmental conditions. Adaptation’s influence on the solution’s performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict: the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.