Simulation Experiments of Different Metaheuristics Algorithms using Benchmark Functions: A Performance Study

Abstract

Metaheuristics have been commonly used in several engineering optimizations, they prerequisite reduced time to converge and produce a better-improved solution. The most applied are Evolutionary Algorithms. Many complex problems are no longer considered difficult due to swarm intelligent optimization algorithms that supply rapid and reliable methods for solutions. and this returns to its features such as robustness, flexibility, self-organization, parallel, and distributive. In this paper, a comparative study among five metaheuristics algorithms in terms of convergence, robustness, and computing time is accomplished, and three benchmark functions are applied to perform simulation experiments with Genetic Algorithm (GA), Firefly Algorithm (FA), Particle Swarm Algorithm (PSO), Invasive Weed optimization (IWO), and Grey Wolf optimizer (GWO). The experimental results show that GE provides more accuracy for complex optimization problems, while the GWO and PSO are better in terms of convergence speed.