Optimization Algorithms for Solving Large Scale Engineering Problems

Global optimization helps determine a problem's optimal value. Exploration and exploitation are utilized in the optimization process to ‎explore and refine the solution space. Intense exploitation accelerates the rate at which agents converge to the global optimum, whereas a ‎strong exploration capability is necessary to navigate the multi-modal search spaces. Based on the idea of mass attraction, the Gravitational ‎Search Algorithm (GSA) is a very promising heuristic algorithm that draws inspiration from physics. For real-world situations, it has been ‎routinely employed to determine the global optima. Due to its inherent strong exploration capabilities and greater diversification power, GSA ‎is less likely to experience local minima dropping.‎ Additionally, GSA stores the best candidate solutions using an elitist approach. Thus, it shields optimal agents from interference from local ‎minima. However, when tackling complex problems, GSA encounters obstacles such as premature convergence and entrapment in local ‎minima‎.