Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems
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 o...
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MDPI AG
2020-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/6/2075 |
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| author | Shih-Cheng Horng Shieh-Shing Lin |
| author_facet | Shih-Cheng Horng Shieh-Shing Lin |
| author_sort | Shih-Cheng Horng |
| collection | DOAJ |
| description | 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 <i>N</i> significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the <i>N</i> 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. |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-20T01:18:12Z |
| publishDate | 2020-03-01 |
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| spelling | doaj.art-955350e8cb294ce8845dc61c46bd3cbf2022-12-21T19:58:32ZengMDPI AGApplied Sciences2076-34172020-03-01106207510.3390/app10062075app10062075Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization ProblemsShih-Cheng Horng0Shieh-Shing Lin1Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, TaiwanDepartment of Electrical Engineering, St. John’s University, New Taipei City 251303, TaiwanThe 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 <i>N</i> significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the <i>N</i> 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.https://www.mdpi.com/2076-3417/10/6/2075stochastic inequality constraintsordinal optimizationelephant herding optimizationtensor product splineoptimal computing budget allocationmulti-skill call centerservice level |
| spellingShingle | Shih-Cheng Horng Shieh-Shing Lin Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems Applied Sciences stochastic inequality constraints ordinal optimization elephant herding optimization tensor product spline optimal computing budget allocation multi-skill call center service level |
| title | Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems |
| title_full | Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems |
| title_fullStr | Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems |
| title_full_unstemmed | Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems |
| title_short | Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems |
| title_sort | coupling elephant herding with ordinal optimization for solving the stochastic inequality constrained optimization problems |
| topic | stochastic inequality constraints ordinal optimization elephant herding optimization tensor product spline optimal computing budget allocation multi-skill call center service level |
| url | https://www.mdpi.com/2076-3417/10/6/2075 |
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