Issues of Generality, Reliability and Efficiency in Optimum Design

Abstract

Optimal design of large systems in the interdisciplinary design environment needs reliable algorithms. Many algorithms have been developed and evaluated for structural optimization. In these algorithms, efficiency has been given priority over reliability and generality. To have efficiency, approximations are introduced into the algorithms. With such approximations, many algorithms loose their robustness and applicability to complex problems. In summary, approximate methods are efficient but unreliable, inaccurate and not general. Globally convergent (robust) methods are general, accurate and reliable but need more computational effort. My contention is that we should relax the efficiency “constraint” for optimum design of practical systems. I am not saying that we should use inefficient algorithms. What I am advocating is that reliability should be given more weightage over efficiency in practical design environment. We should concentrate on developing reliable algorithms that are generally applicable. For complex and interdisciplinary systems, reliability of algorithms is essential. Unreliable algorithms can be actually more expensive because they require more user interaction and time, resulting in overall inefficiency. Use of optimization in general design environment also needs reliable algorithms.