Nongradient Methods in Multidisciplinary Design Optimization-Status and Potential

Abstract

A number of multidisciplinary design optimization (MDO) problems are characterized by the presence of discrete and integer design variables, over and beyond the more traditional continuous variable problems. In continuous variable design problems, the design space may be nonconvex or even disjointed. Furthermore, the number of design variables and constraints may be quite large. The use of conventional gradient-based methods in such problems is fraught with hazards. First, these gradient-based methods cannot be used directly in the presence of discrete variables. Their use is facilitated by creating multiple equivalent continuous variable problems; in the presence of high dimensionality, the number of such problems to be solved can be quite large. Second, these methods have a propensity to converge to a relative optimum closest to the starting point, and this is a major weakness in the presence of multimodality in the design space. This paper primarily focuses on the use of nontraditional optimization methods in such problems, broadly classified today as soft computing strategies. These methods include techniques such as simulated annealing, genetic algorithms, Tabu search, and rule-based expert systems. It also examines issues pertinent to using these methods in MDO problems.