[1]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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Teaching-learning-based optimization algorithm based on random crossover-self-learning strategy

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 313-322 Column: 学术论文—智能系统 Public date: 2021-03-05
Title:
Teaching-learning-based optimization algorithm based on random crossover-self-learning strategy
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
Keywords:
swarm Intelligence; teaching-learning-based optimization; random crossover; “self-learning” strategy; Benchmark function; non-origin-optimal; diversity analysis
CLC:
TP391
DOI:
10.11992/tis.201910045
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.
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Last Update: 2021-04-25

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