Stochastic optimization using grey wolf optimization with optimal computing budget allocation

Stochastic optimization problems exist widely in many manufacturing and service systems. Due to the stochastic nature, these problems usually have no analytical solutions and are difficult to solve. This research proposes a hybrid approach that integrates the grey wolf optimization algorithm and the simulation optimization framework. In this hybrid approach, the grey wolf optimization algorithm is used to search for candidate solutions from the solution space, while the simulation helps the algorithm to identify the desired solutions such that the search is guided to more promising regions. To enhance the efficiency of simulation, this work designs a computing budget allocation rule that helps the grey wolf optimization algorithm to select the elite candidate solutions in each iteration. The proposed computing budget allocation rule is then integrated with the grey wolf optimization algorithm to solve stochastic optimization problems. Numerical experiments confirm that the proposed computing budget allocation rule performs better than extant allocation rules, and can find the better solution for stochastic optimization problems using fewer iterations by integration with the grey wolf optimization algorithm. (C) 2021 Elsevier B.V. All rights reserved.