1. INTRODUCTION Optimization, in simple terms, means minimizing the costs incurred and maximizing the profit as well as the utilization of resources. EAs are population-based metaheuristic optimization algorithms (means optimizing the problem by iteratively trying to improve the solution with respect to the given quality measure) that often work well in approximating solutions to all types of problems because they make no assumptions about the underlying evaluation of the fitness function. There are many EAs available viz. Genetic Algorithm (GA) [1], Artificial Immune Algorithm (AIA) [2], Ant Colony Optimization (ACO) [3], Particle Swarm Optimization (PSO) [4], Differential Evolution (DE) [5 , 6], Harmony Seeking (HS) [7], Bacteria Foraging Optimization (BFO) [8], Dragging Frog Leap (SFL) [9], Artificial Bee Colony (ABC) [10, 11] ,Biogeography Based Optimization (BBO) [12], Gravity Search Algorithm (GSA) [13], Grenade Explosion Method (GEM) [14] etc. To use any EA, a decision model You need to build a problem that specifies: 1) The decisions to be made, called decision variables, 2) The measure to optimize, called the objective, and 3) Any logical restrictions on potential solutions, called constraints . These 3 parameters are necessary while creating any optimization model. The solver will find values ​​for the decision variables that satisfy the constraints by optimizing (maximizing or minimizing) the objective. But the problem with all the above EAs is that, to obtain an optimal solution, in addition to the necessary parameters (explained above), many specific parameters of the algorithms must be managed appropriately. For example, in the case of GA, the adjustment of the specific parameters of the algorithm such as crossover rate (or probability, PC), mu...... middle of paper... the algorithm is identified and modified appropriately, using OpenMP, you can easily take advantage of the multi-core CPU functionality and maximize the utilization of all cores of the multi-core system necessary from the optimization point of view (that says, maximize resource utilization). This paper contributes in this direction and undertakes a detailed study investigating the effect of the number of nuclei, size size, population size, and problem complexity on the speedup of the TLBO algorithm. In the remainder of this article, we give a brief literature review on TLBO and its applications. Next, we discuss the possibilities of modifying a TLBO to make it suitable for parallel implementation on a multi-core system. Then, we present results on some test problems of different complexity and show appreciable speedups using the proposed algorithm.