Engineering design optimization as a fuzzy control process

The optimization process can be viewed as a closed-loop control system. Traditional “controllers”, the numerical optimization algorithms, are usually “crisply” designed for well defined mathematical models. However, when applied to engineering design optimization problems in which function evaluations can be expensive and imprecise, very often the crisp algorithms will become impractical or will not converge. A common strategy for designers is to monitor the optimization process and keep “tuning” it in an interactive manner, using their judgment on the information obtained from the previous iterations, and their knowledge of the problem. This paper presents how the heuristics of this human supervision can be modeled into the optimization algorithms using fuzzy control concept. A fuzzy version of sequential linear programming, which is very popular in engineering design optimization, is used to demonstrate this idea. Fuzzy rules, which describe the human supervision during the optimization process, are combined with the numerical rules of the original algorithm to refine the output of each iteration. Simple numerical examples are used to show the feasibility and practicality of this approach