计算机应用研究2017，Vol.34Issue(7)：1939-1945,1956,8.DOI:10.3969/j.issn.1001-3695.2017.07.004
面向高维复杂多模态问题的教与学优化求解算法
Teaching-learning-based optimization algorithm for solving high-dimensional complex optimization problems with multimodality
摘要
Abstract
Aiming at the shortcomings for solving complex multimodal optimization problems,such as searching at local optimum and premature convergence,this paper presented a modified teaching-learning-based optimization (MTLBO) algorithm to enhance the capability of global exploration power.In MTLBO,it modified the strategies of "teaching" phase and "learning" phase,and it proposed a new "self-learning" strategy to balance the global exploration power and the local exploitation power,which could enhance the innovation ability of learners for improving the global exploration power of MTLBO.Finally,it employed ten complex benchmark functions to investigate the performance of MTLBO,the experimental results indicate that,compared with other five state-of-the-art TLBO variants and three other type of algorithms (SaDE,CLPSO,NGHS),the MTLBO algorithm has stronger global exploration power and more robustness on performance.关键词
改进的教与学优化算法/"自学"机制/复杂多模态优化问题Key words
modified teaching-learning-based optimization/"self-learning"mechanism/complex multimodal optimization problem分类
信息技术与安全科学引用本文复制引用
拓守恒,雍龙泉,黎延海,邓方安..面向高维复杂多模态问题的教与学优化求解算法[J].计算机应用研究,2017,34(7):1939-1945,1956,8.基金项目
国家自然科学基金资助项目(11401357) （11401357）
陕西省教育厅科研计划项目(16JK1157) （16JK1157）
陕西理工大学王巍院士工作站科研项目(fckt201509) （fckt201509）
陕西省青年科技新星项目(2016KJXX-95) （2016KJXX-95）
