基于人工选择的组合电路优化算法

针对一般组合电路的优化算法复杂、优化过程时间长、优化效率偏低等问题,提出一种人工选择方式下的组合电路优化算法。该算法模拟物种进化时的家养模式,将最小项作为基因,函数表达式作为染色体,把逻辑电路的优化过程演变为遵循电路定律的基因变异、重组、寻优的过程。算法通过有利的变异条件,提高了算法的收敛速度和效率。通过与简单免疫、多目标遗传、自适应免疫算法的实验比较,证明了该算法的有效性和优越性。

Optimization algorithm based on combinational circuits of artificial selection

To overcome the difficulties, such as complex optimization algorithm, long time of the optimization process, and relative low efficiency, in automatic designing common combined logical circuit, this paper proposed an optimization algorithm based on the combinational circuits of artificial selection. This algorithm simulate the artificial feeding pattern of biology evolution, the logic minimization as the genes, the function expressions as chromosomes, and the optimization process of the logical circuit as the process of genetic variation, reorganization and gene mutation optimization abided by the laws of circuit. This algorithm improved the convergence speed and efficiency by the favorable variation condition. Compared with the simple immune algorithm, a multi-objective genetic algorithm or the adaptive immune algorithm of experimental comparison, proved this algorithm to be valid and superior.