Optimization and Simulation Using Metaheuristic Algorithms.

Abstract

In recent years, various Heuristic optimization methods have been developed. Some of these algorithms are inspired by swarm behaviors in nature others but Evolution and others are Physics based. In the first chapter of this work we covered the basic notions and definition that helped us understand the operating of metaheuristics In the second chapter we studied the methods that we were going to base our applications on as well as benchmark functions we used to test the performances of the said methods. And finally in the last chapter we put three metaheuristics to test by applying them to both constrained and unconstrained benchmark functions we showcased and discussed the results and compared them with previous studies. The three metaheuristics are : 1. The particle swarm algorithm inspired by swarm behavious. 2. The differential evolution inspired by evolutionary process. 3. The gravitational search algorithm inspired by Newtonian physics. The three algorithms showcased in this report have all proven themselves efficient methods to turn to when it comes to not only on unconstrained problems but also on constrained problems. For these reasons it would be very interesting to apply these methods on real existing physics problems