According to the slow convergence speed and low searching precision of traditional backtracking search optimization algorithm, this paper proposed an improved algorithm based on cellular automaton and orthogonal experimental design. Firstly, the algorithm introduced orthogonal experimental design method into the crossover operator to obtain representative high-quality offspring individuals. Then based on the neighbor model of cellular automaton, it carried out the orthogonal crossover operation of multiple parents in the domain for individuals, which was beneficial to improve the mining capacity and search efficiency of the algorithm. Finally, in order to balance the global searching and local searching ability of the algorithm, it introduced the dynamic proportional weight of excellent individuals into the cross-population to select and update them, with adopted a new dynamic variation equation. The simulation experiments selected 12 standard test functions and compared with 6 other well-behaved algorithms, the results show that the improved algorithm has obvious advantages in convergence speed and optimization accuracy.

[2020-02-05] Printed Article /doc/2018.07.0520/v2