Error Optimization in Random Number Generation Using ABC Algorithm

Abstract

Optimization is an effort to find the best option in the settings of the objective function. The goal of this study is to optimize multivariable functions using Artificial Bee Colony (ABC) which is one of the most current swarm intelligence-based algorithms, simulating the foraging activity of honeybees. The performance of the ABC algorithm has been measured on the minimization process of 22 benchmark functions including the popular Griewank, Rastrigin, Sphere, and Rosenbrock. The experimental results demonstrate that the ABC algorithm has approximated the actual value with an accuracy percentage of 98.85.