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Optimization is an effective tool for optimum utilization of existing resources so as to improve quality, productivity and to reduce the cost. The majority of the real-world situations have multiple objectives that conflict with each other. Hence multiple objectives or criteria need to be optimized effectively and simultaneously for achieving the best output. Hence, many evolutionary algorithms like Pareto Optimization, Non-Sorted Genetic Algorithm (NSGA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), etc. have been developed in the past for this purpose. However, in order to obtain more accurate predictions, these techniques are continuously being modified to make them evolutionary in nature resulting in newer multi-objective optimization techniques. Teaching-Learning Based Optimization (TLBO) and JAYA are two state-of-the-art multi-objective optimization techniques. This paper presents a review of TLBO and JAYA multi-objective optimization techniques with an emphasis on key insights into the methodology and algorithms, followed by their application in different fields. It is observed that these techniques yielded better performance than the existing ones. |
