摘要
辅助函数法在求解多峰全局优化问题时通常存在参数敏感和难以求解高维问题的缺陷,针对这两类缺陷提出了一种智能辅助函数法.首先,利用平滑函数来消除比当前找到的最好解差的解,在此基础上设计了一种新的辅助函数,该辅助函数仅包含一个易于设置的参数,而且可以有效地避免由于参数设置不当导致的"Mexican hat"效应;然后,分析了提出的辅助函数的性质,将辅助函数与智能优化技术相结合来提高算法的搜索能力,从而保证了算法在求解高维问题时能够成功找到全局最优解;最后,在数值实验中,利用13个标准测试函数来测试算法中参数的影响以及算法的搜索性能.
When solving multimodal global optimization problems,many auxiliary function methods are sensitive to their parameters and of great difficulty in dealing with high dimensional problems.Aiming to overcome these two disadvantages,a new intelligent auxiliary function method is proposed in this paper.Firstly,the smoothing function is employed to eliminate the solutions worse than the best one found so far.Based on the smoothing function,a novel auxiliary function is constructed,in which there is only one easilyadjusted parameter.This auxiliary function can avoid the unwilling ＂Mexican hat＂ effect caused by improper parameter settings.Then,properties of the auxiliary function are analyzed.In order to improve the searching ability,the proposed auxiliary function and intelligent optimization techniques are assembled in the designed method,which can help the method deal with high dimensional problems.Finally,13 different benchmarks are used to test the influence of the parameter and the performance of the searching method.Experimental results indicate the effectiveness of the proposed method.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2013年第5期148-156,共9页
Journal of Xidian University
基金
国家自然科学基金资助项目(61272119
61203372)
中央高校基本科研业务费专项资金资助项目(K5051303009)
关键词
多峰优化
全局优化
极小化
智能辅助函数法
multimodal optimizatioin
global optimization
minimization
intelligent auxiliary function method
