Evolutionary algorithm

Metaheuristic

Algorithm

Process (computing)

Convergence (economics)

Archaeology

Operating system

Computation

Economics

Particle swarm optimization

Creatures

Computer science

Mathematical optimization

Swarm behaviour

Evolutionary computation

Swarm intelligence

Mathematics

Natural (archaeology)

Artificial intelligence

Heuristic

Economic growth

Machine learning

History

Neurocomputing

Over past few decades, swarm intelligent algorithms based on the intelligent behaviors of social creatures have been extensively studied and applied for all kinds of optimization areas. Teaching–learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of TLBO is divided into two phases and each phase executes iterative learning operation. Since its introduction by Rao and his team in 2010, TLBO has attracted more and more researchers' attention because of some of its strengths such as simple concept, without algorithm-specific parameters, rapid convergence and easy implementation yet effectiveness. In this paper we attempt to provide a brief review of the basic concepts of TLBO and a comprehensive survey of its prominent variants and its typical application, and the theoretical analysis conducted on TLBO so far. We hope that this survey can be very beneficial for the researchers engaged in the study of TLBO.

A survey of teaching–learning-based optimization