Research on Improved Genetic Algorithm for Low-dimensional and Multimodal Function Optimization

摘要：

In order to overcome the disadvantages that simple genetic algorithm had in low-dimensional and multimodal function optimization, an improved algorithm was proposed. The new algorithm divided the search interval into different areas by method of orthogonal decomposition, produced initial populations that had different individual distribution density by using interval algorithm, and gave each individual an attribute that marked the area which it was in and guaranteed there was more than one individual in each area during the operating process of the algorithm. Particle swarm algorithm was introduced to the mutation operation of genetic algorithm to overcome the shortage of genetic al gorithm, that was, the poor search ability in local areas, especially those that had low individual distribution density. When applied to six-hump camel back function and Branin RCOS function optimization, the simulation results show that, under the same experiment condition, in contrast to other improved algorithms, the new one improves the probability of searching to all extreme points, and has stronger ability of low-dimensional multi-modal function optimization, on the premise that the convergence precision is not affected.

关键词： Genetic Algorithm Interval Algorithm Particle Swarm Algorithm Six-hump Camel Back Function Branin RCOS Function

作者: Liqing Xiao Huaxiang Wang Xiaoju Xu

作者单位: School of Electrical Engineering & Automation,Tianjin University,Nankai District,Tianjin 300072,Chin School of Electrical Engineering & Automation,Tianjin University,Nankai District,Tianjin 300072,Chin epartment of Information & Electrical Engineering,Xuzhou Institute of Technology, Xuzhou Jiangsu 221

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 3918-3922

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）