摘要
遗传算法是一种借鉴生物界自然选择和自然遗传机制的高度并行、随机、自适应搜索方法,作为优化方法具有明显的优势。通常的遗传算法在实际应用中容易出现过早收敛和搜索结果在最优值附近摆动问题。针对过早收敛提出了采用随机试验法来防止算法陷入局部最优,而针对搜索结果摆动采用动态改变搜索范围的方法来提高优化结果精度,并编制程序对2个著名的优化方法测试函数进行优化计算,测试结果表明,该改进的遗传算法是有效的,不会陷入局部最优,并大大提高了优化结果的精度。
As an optimal method, Genetic Algorithm has obvious advantages, which is based on the nature selection and genetic transmission mechanisms such as high collateral,stochastic,self-reliance. but when in practical application, it usually has problems of premature convergence and result swing near optimum value.To solve the problem of premature convergence, the method called Monte-Carlo is adopted to prevent the algorithm from local optimal, and to the problem of result swing, the method changing the hunting zone dynamically is proposed to improve the accuracy of the optimal result. Further more, it devises programs to optimize the test functions of two famous optimal methods. The test results indicate that the improved Genetic Algorithm is valid, which can not only avoid local optimal but also improve the accuracy of the optimal result.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第5期5-7,共3页
Journal of Chongqing University
基金
国家自然科学基金资助项目(50475064)
关键词
遗传算法
浮点数编码
过早收敛
随机试验法
genetic algorithm
float encoding
premature convergence
Monte-Carlo
