Teaching-Learning based optimization (TLBO) is population-based optimization which is used to solve continuous non-linear optimization problems and multiobjective optimization complications. TLBO algorithm can be categorized into two phases. The Teacher Phase involves learning from the best solution and the learner phase facilitates peer-to-peer learning. An elitist concept is introduced TLBO to solve complex constrainedly optimization complications. Subsequently an enhanced (TLBO) is developed to resolve unconstrained optimization problems. Weighted TLBO is used to increase convergence rate. Quasi-oppositional TLBO algorithm is incorporated to tackle multi-objective optimal reactive power dispatch (ORPD) problem by stability of voltage and reducing actual power loss. The combined heat and power dispatch (CHP) problem is effectively solved using an Oppositional TLBO. Modified TLBO is presented for global numerical optimization. TLBO algorithm successfully resolves the Multi Objective Optimal Power Flow (MOOPF) considering different system requirements. The ORPD problem is addressed using a Double-differential evolution (DDE) algorithm and TLBO collectively. Orthogonal design with a new selection strategy is applied to decrease the number of generations. Through resolution of short-term hydrothermal scheduling (HTS) problems in practical power system, the capability of TLBO is strengthened. A modified teaching factor and mutation operator are introduced into TLBO to adjust the convergence speed. Optimization Ethylene Cracking Furnaces Operation using self-adaptive multi objective optimization is recommended Future research directions for TLBO are developing adaptive parameter, hybridizing TLBO with other optimization techniques and enhancing exploration and exploitation balance.