Quality-Assisted Multi-Objective Multidisciplinary Genetic Algorithms
S. Gunawan, Shapour Azarm, Jin Wu, Art Boyars · AIAA Journal · 2003
A new method is presented to solve multi-objective multidisciplinary optimization (M-MDO) problems. This M-MDO method is applicable to multi-objective optimization problems that can be decomposed hierarchically into multi-objective subproblems and whose objective functions are either separable or additively separable. In the decomposition, the subproblems may have both common and unique objectives. The method uses a multiobjective genetic algorithm (MOGA) to optimize the multi-objective subproblems; hence, it is referred to as a multi-objective multidisciplinary genetic algorithm (M-MGA). It is shown that for any Pareto point of the original (single-level) problem, M-MGA generates at least one point that is noninferior with respect to that Pareto point. Also a comparison is shown between the computational complexity of M-MGA and a single-level MOGA in terms of number of functions calls. The M-MGA is demonstrated by two engineering examples: the design of a speed reducer and the design of a payload for an undersea autonomous vehicle. In both examples, the generated solutions are similar to solutions generated by solving the examples as single-level problems. M-MGA produces relatively the same solutions from one M-MGA run to another.