摘要
针对随机优化问题的不确定性,提出一类基于假设检验的模拟退火算法.该方法通过多次评价来合理估计解的性能,利用假设检验减少重复性搜索,采用突跳性搜索避免局部极小,并通过温度控制调节突跳能力.数值仿真研究了假设检验、性能估计、噪声幅度对算法性能的影响,其结果验证了该方法的有效性和鲁棒性.
According to the non-deterministic property of stochastic optimization problems, a class of simulated annealing approach based on hypothesis test is proposed. In the approach, reasonable estimated performance is provided by multiple evaluations, and repeated search is decreased by hypothesis test. The case of being trapped in local minimum can be avoided by jumping probability. The jumping ability can be adjusted by controlling the temperature. The effects of hypothesis test, the performance estimation and the magnitude of noise on the performance of the approach are studied, and the effectiveness and robustness of the proposed approach are demonstrated.
出处
《控制与决策》
EI
CSCD
北大核心
2004年第2期183-186,共4页
Control and Decision
基金
国家自然科学基金资助项目(60204008
60374060)
973计划(2002CB312200).
关键词
随机优化
假设检验
模拟退火
Algorithms
Computer simulation
Optimization
Statistical methods
