基于元胞自动机的动态回溯搜索优化算法. (Chinese)

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 intrcxluced orthogonal experimental design method into the crossover operator to obtain representative high-quality offspring individuals. Then based on the neighbor mcxlel 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. [ABSTRACT FROM AUTHOR]

针对传统回溯搜索优化算法存在收敛速度慢、搜索精度不高等问题，提出了一种基于元胞自动机和正 交实验设计的改进算法。首先将正交实验设计方法引入算法的交叉算子中，得出具有代表性的优质子代个体； 然后在元胞自动机邻居模型的基础上，对个体展开领域内多父代正交交叉操作，提高算法的开采能力和搜索效 率；最后对参与交叉的种群引入动态优秀个体比例权重进行选择更新，并采用新的动态变异方程，平衡算法的全 局搜索和局部搜索能力。通过对12个标准测试函数进行仿真实验，并与其他六种表现良好的算法进行比较，结 果表明，改进的算法在收敛速度以及寻优精度方面都具有明显优势。 [ABSTRACT FROM AUTHOR]

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