摘要
针对传统模拟退火算法计算效率较低的问题,文中将布朗运动和模拟退火相结合,提出一种智能启发式算法.该算法将布朗运动中粒子运动时间和模拟退火温度联系在一起,布朗运动的粒子运动时间等效于退火温度的倒数,通过理论分析得到基于布朗运动的邻域函数模型以及相应的温度下降函数.温度下降函数具有更快的退温特性,保证算法执行过程中具有更高的效率.数值实验结果表明,该算法具有搜索速度快、稳定性高和易于实现的特点,能显著提高求解全局优化问题的计算效率.
A new intelligent heuristic algorithm which is a combination of the Brownian motion and simulated annealing measure is proposed to improve the search efficiency of traditional algorithm.The algorithm has established a connection between Brownian particle motion time and simulated annealing temperature.Reciprocal value of annealing temperature is equivalent to Brownian particle motion time.A neighborhood function model based on the Brownian motion and the corresponding temperature of descent function are obtained through the theory analysis.The proposed annealing temperature of descent function can be fast,and it has a high efficiency.The numerical results show that the algorithm is fast,stable and easy to realize and can improve significantly the computational efficiency for solving the global optimization problem.
出处
《计算机学报》
EI
CSCD
北大核心
2014年第6期1301-1308,共8页
Chinese Journal of Computers
基金
国家自然科学基金(61203369
61204122)
福建省科技计划重点项目(2013H0029)
华侨大学科研基金项目(11HZR03)
厦门市重大科技创新平台项目(3502Z20131019)资助~~
关键词
布朗运动
模拟退火
启发式算法
退火温度
Brownian motion
simulated annealing
heuristic algorithm
annealing temperature
