CAJ | 학술논문

为了避免遗传算法种群中个体过早陷入局部最小，在以往随机初始种群的基础上提出一种均分法，使得初始种群随机平均地分为若干个子种群，形成小生境，这样既维持了种群的多样性，也使得种群中的个体不会过早出现早熟现象，更提高了算法的收敛速度。同时采用了自适应技术控制交叉和变异的概率，使得算法能更快速地找到最优解。仿真结果表明，与传统的遗传算法优化 RBF网络相比较，新算法的迭代次数更少，精度更高，大大提高了收敛速度。
위료피면유전산법충군중개체과조함입국부최소，재이왕수궤초시충군적기출상제출일충균분법，사득초시충군수궤평균지분위약간개자충군，형성소생경，저양기유지료충군적다양성，야사득충군중적개체불회과조출현조숙현상，경제고료산법적수렴속도。동시채용료자괄응기술공제교차화변이적개솔，사득산법능경쾌속지조도최우해。방진결과표명，여전통적유전산법우화 RBF망락상비교，신산법적질대차수경소，정도경고，대대제고료수렴속도。
In order to avoid the population premature into local minimum,a new averaging method based on a ran-dom initial population was introduced into the genetic algorithm. The initial population is stochastically divided into several sub populations to form niches,with the purpose to maintain the population diversity,make the individuals in a sub population not display prematurity phenomenon,and improve the convergence speed of the algorithm as well. The adaptive technique is employed to control the crossover and mutation probability,therefore the algorithm can find the optimal solution quickly. Simulation results show that,compared with traditional RBF neural network opti-mized by genetic algorithm,the new algorithm is characterized by less iterations,higher precision,and greatly im-proved convergence speed.