Abstract
Swarm intelligence algorithms and evolutionary algorithms have been two well-known optimization approaches since the beginning of optimization. These population-based meta-heuristic algorithms are applied to a wide range of challenging multi-national computing challenges in the real world. Recently conducted studies on various multi-goal optimization techniques, on the other hand, shows that those inherently evolved meta-heuristics are incapable of dealing with multi-dimensional problems due to flaws. R.V. Rao proposed the Teaching-Learning Based Optimization (TLBO) method as a revolutionary population-based completely meta-heuristic for evaluating this type of situation in 2011. TLBO's applicability has surpassed many milestones since its inception, compared to today's advanced meta-heuristics for use in a number of engineering tasks.
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