The TLBO algorithm works by maintaining a population of solutions, called learners. The learners are initialized randomly. In each iteration of the algorithm, the learners are evaluated and the best learner is identified. The best learner is then used to create a new solution, called a teacher. The teacher is created by making small changes to the best learner. The new teacher is then added to the population of learners.
The learners are then updated by comparing them to the teacher. The learners that are closest to the teacher are updated more than the learners that are further away. This process is repeated until a termination criterion is met.
TLBO has been shown to be effective in solving a variety of optimization problems. It is a relatively simple algorithm to implement and it is not as computationally expensive as some other metaheuristic algorithms.
Here are some of the advantages of using TLBO:
It is a simple and easy-to-implement algorithm. It is not as computationally expensive as some other metaheuristic algorithms. It has been shown to be effective in solving a variety of optimization problems. Here are some of the disadvantages of using TLBO:
It may not be as effective as some other metaheuristic algorithms for some problems. It may require a large number of iterations to converge to a good solution. Overall, TLBO is a powerful and versatile optimization algorithm that can be used to solve a wide variety of problems. It is a good choice for problems where simplicity and ease of implementation are important.