A joint optimization method of genetic algorithm and numerical algorithm based on MATLAB

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the genetic algorithm toolbox. In order to compare with each other, the parametric model of the planar truss is also established by the ANSYS Parametric Design Language and solutions are obtained using the first-order method native to ANSYS. The comparison of the results shows that genetic algorithms do not always display better properties than other algorithms for some problems. Finally, a joint optimization method which combines MATLAB genetic algorithm toolbox and the numerical algorithm based on the quasi-Newton method is proposed. Then the method is identified through the numerical example of the two-bar truss. The simulation results indicate that the joint optimization method can always converge to the global optimal solution.

I. Introduction

Genetic algorithms, based on the mechanics of natural selection and genetics, combine Darwin's theory of evolution based on survival of the fittest and a systematic information exchange guided by random operators to form a robust search procedure. The optimization process gets its dynamic by developing new generations of potential solutions and evaluating the degree of fitness of each generation and allowing it to proceed if it satisfies specific selection criterion which is usually based on a fitness-proportional selection [1].