Study of convergence in metaheuristics algorithms

Abstract

Artificial intelligence (AI) system purpose is to help humans solve problems. This branch of science became famous less than a hundred years ago. Since then, it has gained momentum and scale. This area is currently associated with many methodologies, some of which are called metaheuristics algorithms. In this work, we will look at several metaheuristics algorithms. Comparison of algorithm solutions will be performed. We compare the accuracy of the results, the speed of the solution, and other parameters. They will solve one of the classic NP problems. This problem is named a scheduling problem. This paper presents an approach for enhancement of this balance in single solution metaheuristics applied to solve two processors scheduling problem generated during metaheuristic search. We compare Simulated Annealing (SA) algorithm with our develop modification amongst to other well-known metaheuristics like a genetic algorithm (GA) and artificial ant colonies algorithm (ACA) taken from the source of literature.