BibTeX:

@article{IJIRSTV1I11177,
     title={Teaching Learning based optimization},
     author={K. Satheesh Kumar and R. Harris Samuel},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={11},
     pages={413--419},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I11177.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The main aim of the project is to develop a new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, for the optimization of mechanical design problems. The process of TLBO is divided into two parts. The first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. Teacher tries to reach best harmony on the output of learners in a class, which can be obtained through their grades considered as the output. Output is appraised by means of exam conducted by the teacher. TLBO is a population based algorithm where a group of students (i.e. learners) is considered as population and the different subjects offered to the learners is analogous with the different design variables of the optimization problem. The grades of a learner in each subject represent a possible solution to the optimization problem (value of design variables) and the mean result of a learner considering all subjects corresponds to the quality of the associated solution (fitness value).The best solution in the entire population is considered as the teacher. The method is tested on different unconstrained benchmark test functions. The effectiveness of the TLBO method is compared with Genetic Algorithm based on the best solution. Results show that TLBO is 244% more effective than the Genetic Algorithm for the unconstrained optimization problems considered. This novel optimization method is extended to an engineering design optimization problem. The TLBO method is modified for solving Multi objective optimization problems.

optimization, genetic algorithm, teaching, learning, multiobjective