[1]LIU Dixizi,FAN Qinqin,HU Zhihua.Teaching-learning-based optimization algorithm based on chaotic search and weighted learning and its application[J].CAAI Transactions on Intelligent Systems,2018,13(5):818-828.[doi:10.11992/tis.201705017]
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Teaching-learning-based optimization algorithm based on chaotic search and weighted learning and its application

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 5 Page number: 818-828 Column: 学术论文—机器学习 Public date: 2018-09-05
Title:
Teaching-learning-based optimization algorithm based on chaotic search and weighted learning and its application
Author(s):
LIU Dixizi1;  FAN Qinqin1; 2;  HU Zhihua1
1. Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China;
2. MOE Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China
Keywords:
teaching-learning-based optimization; weight learning; heuristic algorithm; chaotic search; global optimization; evolutionary computation; non-cooperative game; Nash equilibrium
CLC:
TP301.6
DOI:
10.11992/tis.201705017
Abstract:
To avoid premature convergence, a teaching-learning-based optimization algorithm based on chaotic search and weighted learning (TLBO-CSWL) is introduced in this study. In the teaching phase, TLBO-CSWL does not only use the individuals obtained by weight learning to guide the population evolution, it also utilizes a normal random number to replace the original uniform random number. In addition, TLBO-CSWL uses a logistics chaotic search strategy to improve its global search ability. Simulation results showed that TLBO-CSWL outperformed other compared algorithms in terms of overall performance. Finally, the proposed algorithm was employed to solve two Nash equilibrium problems of non-cooperative game, and satisfactory results were obtained.
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