[1]黎延海,雍龙泉,拓守恒.随机交叉-自学策略改进的教与学优化算法[J].智能系统学报,2021,16(2):313-322.[doi:10.11992/tis.201910045]
 LI Yanhai,YONG Longquan,TUO Shouheng.Teaching-learning-based optimization algorithm based on random crossover-self-learning strategy[J].CAAI Transactions on Intelligent Systems,2021,16(2):313-322.[doi:10.11992/tis.201910045]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年2期
页码:
313-322
栏目:
学术论文—智能系统
出版日期:
2021-03-05

文章信息/Info

Title:
Teaching-learning-based optimization algorithm based on random crossover-self-learning strategy
作者:
黎延海1 雍龙泉1 拓守恒3
1. 陕西理工大学 数学与计算机科学学院,陕西 汉中 723001;
2. 陕西省工业自动化重点实验室,陕西 汉中 723001;
3. 西安邮电大学 计算机学院,陕西 西安 710121
Author(s):
LI Yanhai1 YONG Longquan1 TUO Shouheng3
1. School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China;
2. Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China;
3. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
关键词:
群体智能教与学优化随机交叉“自学”策略Benchmark函数非原点最优多样性分析
Keywords:
swarm Intelligenceteaching-learning-based optimizationrandom crossover“self-learning” strategyBenchmark functionnon-origin-optimaldiversity analysis
分类号:
TP391
DOI:
10.11992/tis.201910045
摘要:
针对非原点最优的复杂优化问题(最优解不在坐标原点),提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy, CSTLBO)。对标准教与学优化算法的“教阶段”和“学阶段”的空间扰动进行了几何解释,改进了原有的“教阶段”和“学阶段”,并引入随机交叉策略和“自学”策略来提高算法的全局寻优能力。通过使用20个Benchmark函数进行仿真,并与6种改进的教与学优化算法进行结果比较及Wilcoxon秩和检验分析,结果表明CSTLBO算法能有效避免陷入局部最优,具有良好的全局搜索能力,求解精度高,稳定性好。
Abstract:
This paper presents a teaching-learning-based optimization algorithm based on a random crossover–self-learning strategy (CSTLBO) for solving non-origin-optimal complex optimization problems in circumstances in which the optimum solution is outside the origin of the coordinates. First, the spatial perturbation in the “teaching” and “learning” stages of the standard TLBO algorithm is interpreted geometrically, and the original “teaching” and “learning” stages are improved. Then, random crossover and “self-learning” strategies are introduced to improve the global optimization ability of the algorithm. To assess the performance of the CSTLBO algorithm, 20 Benchmark functions were tested, and Wilcoxon rank sum tests were applied to the experimental results of six improved TLBO algorithms. The experimental results indicate that the CSTLBO algorithm is able to avoid premature convergence, and has the advantages in global exploration, solution quality, and stability.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-10-31。
基金项目:国家自然科学基金项目(11502132);陕西省教育厅重点科研计划项目(20JS021);陕西理工大学校级科研项目(SLG1913)
作者简介:黎延海,讲师,主要研究方向为智能优化算法及应用;雍龙泉,教授,博士,主要研究方向为优化理论与算法设计、智能优化算法;拓守恒,副教授,博士,CCF会员,主要研究方向为智能优化算法、生物信息分析与处理。发表学术论文40余篇
通讯作者:黎延海.E-mail:liyanhai@snut.edu.cn
更新日期/Last Update: 2021-04-25