TLBO Algorithm for Global Optimization: Theory, Variants and Applications with Possible Modification - Arpan Garg ,Nitin Mittal , Simrandeep Singh , Nitika Sharma (International Journal of Advanced Science and Technology, 2020)
A large number of nature-inspired methods used in the area of Artificial Intelligence (AI), tosolve a wide range of problems. Computational Intelligence (CI) is one of the most specializedareas of AI with the help of most nature-inspired techniques. Teaching Learning-basedOptimization (TLBO) algorithms are an important method of swarm intelligence (SI)metaheuristics. Due to its noble features over other methods of SI, it was designed for a widevariety of optimization problems. The algorithm was popular around many domains of globaloptimization, such as electrical and electronics engineering, civil engineering, mechanicaldesign, artificial intelligence, physics, computer science, biotechnology and economics. The factthat it only uses regular control parameters and does not need specific algorithms, is one of thekey features of an algorithm. It contributes trendy the direction of the algorithm’s effectiveness,since it reduces the errors caused by excessive parameter tuning. The algorithm is based on thenatural process of learning the classroom. TLBO has recently gained a very high interest inresearch from a variety of domains with large audiences. In this paper, therefore, severalresearchers’ publications using TLBO were reviewed and summarized. Initially, an introductorymaterial on TLBO stays presented that explains the context of the natural structure and itsconceptual basis for optimization. In addition, the recent TLBO version is discussed in detail,categorizing it in a new hybridized and multi-objective model. The main application of TLBO isalso systematically described. The paper provided an analysis of the TLBO algorithm and itscapabilities. This paper describes the comparison of TLBO variants also.