Analysis of Population Based Modern Optimization Techniques

Many real world complex business problems have very large solution space. If an optimal solution is not found and applied then it may result in loss of efficiency, wastage of time and efforts. So, many optimization techniques have been designed to find optimal solution in large solution space. In this paper, we have analyzed a few optimization techniques namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Teaching-learning-based optimization (TLBO). This paper mainly focuses on the merits and de-merits of these algorithm based on their convergence rate, input parameters and Maximum Number of Function Evaluation (MNFE) to achieve global optima.