Selecting the best stochastic systems for large scale engineering problems

Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings

Recent progress on nanomaterial-based membranes for water treatment

Nanomaterials have emerged as the new future generation materials for high-performance water treatment membranes with potential for solving the worldwide water pollution issue. The incorporation of nanomaterials in membranes increases water permeability, mechanical strength, separation efficiency, and reduces fouling of the membrane. Thus, the nanomaterials pave a new pathway for ultra-fast and extremely selective water purification membranes. Membrane enhancements after the inclusion of many nanomaterials, including nanoparticles (NPs), two-dimensional (2-D) layer materials, nanofibers, nanosheets, and other nanocomposite structural materials, are discussed in this review. Furthermore, the applications of these membranes with nanomaterials in water treatment applications, that are vast in number, are highlighted. The goal is to demonstrate the significance of nanomaterials in the membrane industry for water treatment applications. It was found that nanomaterials and nanotechnology offer great potential for the advancement of sustainable water and wastewater treatment.Internal Qatar University grant QUCG-CENG-21/22-4 and Qatar National Research Fund grant NPRP12S-0306-190247.Scopu

A Hybrid PSO-GCRA Framework for Optimizing Control Systems Performance

Optimization is essential for improving the performance of control systems, particularly in scenarios that involve complex, non-linear, and dynamic behaviors. This paper introduces a new hybrid optimization framework that merges Particle Swarm Optimization (PSO) with the Greater Cane Rat Algorithm (GCRA), which we call the PSO-GCRA framework. This hybrid approach takes advantage of PSO's global exploration capabilities and GCRA's local refinement strengths to overcome the shortcomings of each algorithm, such as premature convergence and ineffective local searches. We apply the proposed framework to a real-world load forecasting challenge using data from the Australian Energy Market Operator (AEMO). The PSO-GCRA framework functions in two sequential phases: first, PSO conducts a global search to explore the solution space, and then GCRA fine-tunes the solutions through mutation and crossover operations, ensuring convergence to high-quality optima. We evaluate the performance of this framework against benchmark methods, including EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO. Comprehensive experiments are carried out using metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and convergence rate.  The proposed PSO-GCRA framework achieves a MAPE of 2.05% and an RMSE of 3.91, outperforming benchmark methods, such as EMD-SVR-PSO (MAPE: 2.85%, RMSE: 4.49) and FS-TSFE-CBSSO (MAPE: 2.98%, RMSE: 4.69), in terms of accuracy, stability, and convergence efficiency. Comprehensive experiments were conducted using Australian Energy Market Operator (AEMO) data, with specific attention to normalization, parameter tuning, and iterative evaluations to ensure reliability and reproducibility

Efficient Approach for Selecting the Best Subset of Buffer Profile

Abstract One of the main problems with designing a production line is to find the optimal number of buffers between workstations in order to maximizes the throughput. This problem known as buffer allocation problem. Previous work in this problem focus on selecting a single buffer profile that has the maximum throughput. The objective in this paper would be to selecting from a large number of alternatives, the best subset of buffer profiles where its throughput are at its maximum. The ordinal optimization with optimal computing budget allocation approaches will be used to isolating the best subset of buffer profile, where its throughput is maximum, from the set of all alternatives. Numerical results show that the proposed algorithm finds the best subset of the puffer allocation with high probability and small replications numbers of samples

Selecting the best stochastic systems for large scale engineering problems

Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.</jats:p