Analysing Quantitative Assigment Problem with different Metaheuristic Algorithm

Abstract

The attraction towards the Swarm Intelligence (SI) is increasing day by day in the various research fields. There are many swarm-based
optimizations are being introduced since early 60’s, Evolutionary Algorithms (EA) are the most updated one, Grey Wolf Optimization
Algorithm. All Evolutionary Algorithms have proving their capability to resolve most of the optimization problems. These algorithms are using for training the neural networks in this paper. The main difficulty for any optimization problems is selecting the correct values of parameters to get feasible results. The main idea to get best convergence rate and best performance is to vary the parameters of the algorithms. Quadratic Task Issue (QAP) is a NPhard combinatorial advancement issue, in this manner, settling the QAP requires applying at least one of the metaheuristic calculations. This paper presents a similar report between Meta-heuristic calculations: Comparing the optimization algorithms, Particle Swarm Optimization (PSO), Multi Verse Optimization (MVO) and Grey Wolf Optimization (GWO) before and after tuning the parameters with three different datasets