Improved teaching-learning-based optimization algorithm with Cauchy mutation and chaotic operators

Teaching-Learning-Based Optimization (TLBO) is a population-based intelligent optimization algorithm, which simulates the "teaching" process of teachers to students and the "learning" process of students in the class. In order to solve the problems of slow optimization speed, low optimization accuracy and easy to fall into local optimization, an improved TLBO algorithm based on Cauchy mutation and chaos operators are proposed. Firstly, the dynamic selection of teachers in the "teaching" stage leads to higher class average grades. Learning from the best students in the class during the "learning" phase makes class results more focused. Secondly, after a teaching is completed, Cauchy mutation is carried out to make the algorithm population more diverse so as to get rid of the local optimal solution. Finally, on the basis of Cauchy mutation, chaos theory is introduced into the optimization process of TLBO algorithm, and 10 chaos are embedded in the process of generating random numbers by Cauchy mutation, which enhances its ergo city and irreconcilability to further improve its convergence speed and accuracy. The performance of the proposed improved TLBO algorithm was tested by using 30 benchmark functions in CEC-BC-2017, and finally two engineering design problems (cantilever arm design and pressure vessel design) were optimized. The experimental results show that the proposed TLBO algorithm has significantly improved its convergence speed and optimization accuracy.