A new insight into metaheuristic optimization method using a hybrid of PSO, GSA, and GWO. (October 2022) - Biabani, Fatemeh; Shojaee, Saeed; Hamzehei-Javaran, Saleh
Abstract: This article considers the Particle Swarm Optimization (PSO), Gravity Search algorithm (GSA), and Gray Wolf Optimizer (GWO) robust varieties to obtain fresh insight into solving different types of engineering problems without any adherence to the parameters. In the proposed algorithm, the hybridizing of Gravity search algorithm, Particle swarm optimization, and Gray wolf optimizer (HGPG) utilize to introduce appropriate numerical optimization, and the theoretical findings are supported by solving several benchmark problems. Achieving good results in mathematical functions (CEC2005, 2021) compared with the GWO, GSA, and PSO methods and some other well-known heuristic algorithms shows an enhanced performance of the introduced method compared to the others. The method in a specific case presents a solution to minimize the weight of space truss structures to a logical level and economize the construction of these structures. The results show that the new algorithm made a notable improvement in both exploration and exploitation. Furthermore, a probabilistic metric analysis has also been made of how much better the proposed algorithm is.