A Directed Genetic Algorithm for global optimization

Within the framework of real-coded genetic algorithms, this paper proposes a directed genetic algorithm (DGA) that introduces a directed crossover operator and a directed mutation operator. The operation schemes of these operators borrow from the reflection and the expansion search mode of the Nelder–Mead’s simplex method. First, the Taguchi method is employed to study the influence analysis of the parameters in the DGA. The results show that the parameters in the DGA have strong robustness for solving the global optimal solution. Then, several strategies are proposed to enhance the solution accuracy capability of the DGA. All of the strategies are applied to a set of 30/100-dimensional benchmark functions to prove their superiority over several genetic algorithms. Finally, a cantilevered beam design problem with constrained conditions is used as a practical structural optimization example for demonstrating the very good performance of the proposed method.