Improving the Efficiency of Genetic Algorithms for Frame Designs

Abstract

The focus of this paper is on the development of a design software system that has enough flexibility and capability to search for the most economical steel roof truss design in a reasonable amount of time. This objective is achieved by improving the efficiency and robustness of the genetic algorithm (GA) methodology developed earlier. The effects of schema representation, schema survival, type of crossover, problem definition, the size of the population, and the number of design iterations on the computational expense and the value of the objective function are studied. The research results show that while the final AISl (American Iron and Steel Institute) code-conforming 'best' designs are very close to each other when different starting designs (ground structures) are used, the use of some GA strategies can lead to either highly non-optimal designs or design processes that are computationally expensive. The results also show some other interestink conclusions. The size of the population and the maximum number of design iterations (or generations) need to be at least the size of the chromosome. The schema representation is perhaps one of the most important factors. Depending on the complexity of the initial design (density of the ground structure) and the size of the chromosome, a newly developed Association String strategy has led to a computationally effective GA process when combined with the elitist, one-point and uniform crossover strategies.

Knowledge‐based algorithms in fixed‐grid GA shape optimization

“…Owing to the adaptive nature of the string length, it would be advantageous to adapt the population size accordingly. Also, both empirical and theoretical evidence [27] exist that suggest a linear relationship between population size and string length. As such in FGGA, a linear population sizing rule based on string length was introduced.…”

Optimum Structural Design Using Bio-Inspired Search Methods: A Survey and Applications

“…Chen and Rajan [25] conduct a comprehensive study on the optimization of frame structures using GAs, considering the effects of crossover type, population size, number of generations, type of representation, and so on.…”