Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is on human behavior in which people gain and share their with others. Different types fractional are considered this study. augmented Lagrangian method (ALM) used to handle these constrained by converting them into unconstrained problems. Three examples from the literature transformed deterministic form chance-constrained technique. solved GSK results compared eight other state-of-the-art algorithms. obtained also optimal global solution quoted literature. To investigate performance real-world problem, solid fixed charge transportation problem examined, parameters as random variables. show that outperforms terms convergence, robustness, computational time, quality solutions.