CAJ | 학술논문

遗传算法在接近全局最优解时,存在搜索速度变慢、过早收敛、个体的多样性减少很快、甚至陷入局部最优解等问题.通过在遗传算法中引入模拟退火因子、混沌因子和多样性测度因子,在很大程度上克服了原有遗传算法的早熟、局部搜索能力差的缺点.同时,又能发挥原有遗传算法的强大的全局搜索能力,保证了改进后的混合遗传算法能较好地收敛于其全局最优值.
유전산법재접근전국최우해시,존재수색속도변만、과조수렴、개체적다양성감소흔쾌、심지함입국부최우해등문제.통과재유전산법중인입모의퇴화인자、혼돈인자화다양성측도인자,재흔대정도상극복료원유유전산법적조숙、국부수색능력차적결점.동시,우능발휘원유유전산법적강대적전국수색능력,보증료개진후적혼합유전산법능교호지수렴우기전국최우치.
When approaching the global optimal solution, some shortcomings of the genetic algorithm, such as slow search speed, premature convergence, quick reduction of the diversity of individuals, and even getting into the trouble for local optimal solution, are highlighted. By introducing the simulated annealing factor, chaos factor and diversity measure factor into the genetic algorithm, the original shortcomings, such as the premature convergence and the poor local search capability, are greatly overcome, and meanwhile, the original powerful global search capability of genetic algorithm is maintained. So the hybrid genetic algorithm improved by all the measures can better converge at its global optimal value.