Knowledge-based algorithms in fixed-grid GA shape optimization

Shape optimization through a genetic algorithm (GA) using discrete boundary steps and the xed-grid (FG) nite-element analysis (FEA) concept was recently introduced by the authors. In this paper, algorithms based on knowledge speci c to the FG method with the GA-based shape optimization (FGGA) method are introduced that greatly increase its computational e ciency. These knowledge-based algorithms exploit the information inherent in the system at any given instance in the evolution such as string structure and tness gradient to self-adapt the string length, population size and step magnitude. Other non-adaptive algorithms such as string grouping and deterministic local searches are also introduced to reduce the number of FEA calls. These algorithms were applied to two examples and their e ects quanti ed. The examples show that these algorithms are highly e ective in reducing the number of FEA calls required hence signi cantly improving the computational e ciency of the FGGA shape optimization method.