Biobjective optimization for analytical target cascading: optimality vs. achievability

Analytical target cascading (ATC), a hierarchical, multilevel multidisciplinary methodology, has proved to be an effective solution strategy for complex design problems. ATC decomposition and coordination incorporates compromise between the performance of the system and the demands of the subproblems reflected in their feasibility constraints. Optimal system performance regardless of subproblem feasibility may yield targets that are not achievable by the subproblems. Compromise is needed to accept deterioration of the optimal system performance and to increase the achievability of the targets. Biobjective optimization is used to reconcile system optimality and subproblem achievability of targets while solving the ATC-decomposed problem and generating an overall optimal solution. Three algorithms are proposed for two-level ATC-decomposed problems. The effectiveness of the algorithms is evaluated on mathematical and engineering examples. For a class of ATC problems, the performance of the proposed algorithms is superior to the performance of the ATC methods currently considered to be state of the art.