Comparative Study on Recent Development of Heuristic Optimization Methods

Abstract

In engineering and design problems, various noisy non-linear mathematical optimization problems can't be efficaciously solved by using conventional optimization techniques. But metaheuristic algorithms seem very efficient to approach in these problems and became very popular such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO). Recently, many new metaheuristic algorithms were proposed, but the performance of these algorithms in solving noisy non-linear optimization problems when compared with popular methods still need more of verifications. In this context, two popular algorithms called GA and PSO will be compared with some recent metaheuristic algorithms such as Grey Wolf Optimizer, Firefly Algorithm, and Brain Storm Optimization algorithm in finding optimal solutions of noisy non-linear optimization problems. The results will be compared in terms of accuracy of the best solutions found and the execution time.