Teaching Learning-Based Optimization for Solving CEC2014 Test Suite: A Comparative Study

This is a comparative study of Teaching Learning-Based Optimization (TLBO) as a human-based algorithm against other types of metaheuristic algorithm: single-agent finite impulse response optimizer (SAFIRO), simulated Kalman filter (SKF), particle swarm optimization algorithm (PSO), black hole algorithm (BH), and genetic algorithm (GA), in solving CEC2014 test suite. The TLBO algorithm is inspired by the process of teaching and learning in a classroom. The advantages of TLBO are it only has two main tasks: teaching phase and learning phase and has no parameter setting. The TLBO performance provides a balance between exploration and exploitation. Statistical analysis is then carried out to rank the TLBO results to those obtained by other type of metaheuristic algorithm. The experimental result show that the TLBO algorithm is a promising approach and comparative to SAFIRO and SKF and has better than PSO, BH, and GA.