Hybrid Genetic Algorithms for Optimization of Truss Structures

Abstract

The genetic algorithm (GA) is a new optimization paradigm that models a natural evolution mechanism. The framework of the GA naturally corresponds to a discrete optimization problem. Although the GA is very robust, it is also very computationally intensive and hence slower than other methods. To speed up the convergence, this article proposes a hybrid GA that combines the concept of survival of the fittest with the concept of adaptation. The fully stressed design optimality criterion is employed to play the role of adaptation. Numerical examples show that even though the displacement constraints are active, (1) both average weight and minimum weight obtained by a hybrid GA are less than those obtained by a pure GA, (2) a hybrid GA is more stable than a pure GA, and (3) the speed of convergence of a hybrid GA is superior to that of a pure GA.