摘要
提出了一种将遗传算法与模拟退火算法相结合的新搜索算法。该算法以遗传算法运算流程作为主体流程,并把模拟退火机制融入其中,用以调整优化群体。在进化过程中使用了保留策略,以保存适应度较好的个体。在模拟退火算法的跳变操作过程中使用类似遗传算法变异来实现,先作置反操作,再作前后等长交换操作,以防止陷入局部最优。实验表明,该算法与传统遗传算法相比,提高了进化速度和全局寻优能力。
The new global search algorithms result from the combination of the genetic algorithms and simulated annealing algorithms. The genetic algorithms are served as the main flow of the new algorithms which involve the mechanism of simulated annealing to adjust the optimization population. Reservation strategy is used in evolution process to reserve the individuals which have good fitness. To avoid trapping in local optimum, two steps similar to mutation including inverse operation and exchanging operation were adopted during the flipping operation of simulated annealing. The experiments indicate that the new algorithms can improve the evolution speed and the abilities of seeking the global excellent result.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2003年第1期39-42,共4页
Journal of University of Electronic Science and Technology of China
关键词
模拟
退火机械
遗传算法
进化速度
全局搜索
genetic algorithms
simulated annealing
evolution speed
global search
