A modified teaching–learning-based optimization algorithm for numerical function optimization
In this paper, a kind of modified teaching–learning-based optimization algorithm (MTLBO) is proposed to enhance the solution quality and accelerate the convergence speed of the conventional TLBO. Compared with TLBO, the MTLBO algorithm possesses different updating mechanisms of the individual solution. In teacher phase of the MTLBO, the students are divided into two groups according to the mean result of learners in all subjects. Moreover, the two groups present different updating strategies of the solution. In learner phase, the students are still divided into two groups, where the first group includes the top half of the students and the second group contains the remaining students. The first group members increase their knowledge through interaction among themselves and study independently. The second group members increase their marks relying on their teacher. According to the above-mentioned updating mechanisms, the MTLBO can provide a good balance between the exploratory and exploitative capabilities. Performance of the proposed MTLBO algorithm is evaluated by 23 unconstrained numerical functions and 28 CEC2017 benchmark functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed.
KeywordsTeaching–learning-based optimization Modified teaching–learning-based optimization Exploratory and exploitative capabilities Unconstrained numerical functions CEC2017
The authors make a grateful acknowledgement for the associate editor and the reviewers for their valuable time and thoughtful comments to the improvement of the paper. This work is supported by the National Natural Science Foundation of China (Grant no. 61573306).
- 3.Kennedy J. Particle swarm optimization[M]//encyclopedia of machine learning. Springer US, 2010: 760–766Google Scholar
- 22.Krishnanand KR, Panigrahi BK, Rout PK et al (2011) Application of multi-objective teaching–learning-based algorithm to an economic load dispatch problem with incommensurable objectives[M]//swarm, evolutionary, and memetic computing. Springer, Berlin, pp 697–705Google Scholar
- 24.Rao R, Patel V (2013) Comparative performance of an elitist teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Int J Ind Eng Comput 4(1):29–50Google Scholar
- 25.Rao RV, Patel V (2013) An improved teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica 20(3):710–720Google Scholar
- 28.Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2016) Problem definitions and evaluation criteria for the CEC2017 special session and competition on single objective bound constrained real-parameter numerical optimization, Technical Report, Nanyang Technological University, SingaporeGoogle Scholar