摘要
由于人工免疫算法受到收敛速度相对较慢,局部搜索能力较弱、求解全局最优解需要的群体规模相对较大等因素的影响,本文将最速下降法与人工免疫算法结合,提出了一种新的混合算法.数值实验结果表明,该算法能够找到更优的优化结果,并且在收敛速度上明显优于传统的人工免疫算法.
Artificial immune algorithm used to have disadvantages such as the relatively slow speed of convergence, weak local search capabilities, and the relatively large groups demanded for finding the global optimal solution. In this paper,a new hybrid algorithm is proposed which is based on the combination of the steepest descent method with the artificial immune algorithm. Experimental results show that the new algorithm can help realize better optimization and the speed of convergence of the new algorithm is much higher than that of the traditional artificial immune algorithm.
出处
《内蒙古工业大学学报（自然科学版）》
2009年第2期94-98,共5页
Journal of Inner Mongolia University of Technology：Natural Science Edition
基金
内蒙古自治区自然科学基金(项目编号:200208020104)
内蒙古工业大学重点研究项目(项目编号:ZD200815)
关键词
人工免疫算法
欧式距离
最速下降法
混合算法
artificial immune algorithm
continental distance
steepest descent method
hybrid algorithm
