Multidisciplinary Design Optimization Supported by Knowledge Based Engineering

5Guided Random Search and Network Techniques

There are a number of methods that attempt to find optimum designs using techniques that avoid following a specified search direction exploiting gradient or quasigradient information. The use of directional searches is replaced by a process which either exploits randomized variations in the design variables or avoids the direct variation of design variables altogether by using learning networks. For the first group, the guided random search techniques (GRST), we have selected the genetic algorithm (GA) as a representative one, and similarly, for the second group, the learning network-based methods, we have selected the artificial neural networks (ANN) as a typical example. As with Chapter 4, we do not present the reader with a comprehensive treatment of these algorithms as there is an extensive and comprehensive literature available. Our purpose is to give the reader a sufficient understanding of how these work so that intelligent decisions can be made if these techniques are encountered in the development or use of an MDO system.

5.1 Guide Random Search Techniques (GRST)

GRST methods are attempting to search an entire feasible design space without resorting to exhaustive enumeration and, in principle, are seeking a global optimum. They do not suffer from the disadvantage experienced by search methods discussed in Chapter 4 which lock onto a local minimum and cannot escape from the design region, within which the local optimum lies, ...