Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

Abstract

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods.

Elephant Herding Optimization: Variants, Hybrids, and Applications

“…Horng et al [166] presented a heuristic method coupling EHO with ordinal optimization (EHOO) to resolve stochastic inequality constrained optimization problems. The proposed method utilized an improved elephant herding optimization to achieve diversification with an accelerated optimal computing budget allocation.…”

Integration of Ordinal Optimization with Ant Lion Optimization for Solving the Computationally Expensive Simulation Optimization Problems

“…The one with the optimum performance in the selected subset is the good enough design. We have successfully applied OO framework for simulation optimization problems, such as one-period multi-skill call center [25], pull-type production system [26], and facility-sizing optimization in factory [27].…”