Simulation of COVID-19 outbreaks via Graphical User Interface (GUI)

Background: This research aimed to model the outbreak of COVID-19 in Malaysia and develop a GUI-based model. Design and Methods: The model is an improvement of the susceptible, infected, recovery, and death (SIRD) compartmental model. The epidemiological parameters of the infection, recovery, and death rates were formulated as time dependent piecewise functions by incorporating the control measures of lockdown, social distancing, quarantine, lockdown lifting time and the percentage of people who abide by the rules. An improved SIRD model was solved via the 4th order Runge-Kutta (RK4) method and 14 unknown parameters were estimated by using Nelder-Mead algorithm and pattern-search technique. The publicly available data for COVID-19 outbreak in Malaysia was used to validate the performance of the model. The GUI-based SIRD model was developed to simulate the number of active cases of COVID-19 over time by considering movement control order (MCO) lifted date and the percentage of people who abide the rules. Results: The simulator showed that the improved SIRD model adequately fitted Malaysia COVID-19 data indicated by low values of root mean square error (RMSE) as compared to other existing models. The higher the percentage of people following the SOP, the lower the spread of disease. Another key point is that the later the lifting time after the lockdown, the lower the spread of disease. Conclusion: These findings highlight the importance of the society to obey the intervention measures in preventing the spread of the COVID-19 disease.

Control of TITO processes using sliding mode controller tuned by ITAE minimizing criterion based Nelder-Mead algorithm

Control of multi input and multi output (MIMO) process with interaction is often encountered in process industry. Such MIMO processes are controlled using conventional sliding mode controller (SMC) and tuned by integral square error (ISE) minimizing criterion based Nelder-Mead algorithm. SMC tuned by integral time absolute error (ITAE) minimization criterion based Nelder-Mead algorithm is proposed in this work. Three categories of two inputs and two outputs (TITO) process models are represented in the matrix form, with each of the matrix element representing a first order plus dead time (FOPDT) process. These TITO models are categorized based on the ratio ε, between dead time and time constant of the FOPDT model which forms the matrix element of the TITO model. The performance of conventional SMC is evaluated for these three categories of TITO models, in which the TITO process models with the ratio ε greater than the one, exhibited by poor closed loop performance, whereas the proposed SMC when applied to the these process models delivered superior closed loop performance.

Constrained Eigenvalue Minimization of Incomplete Pairwise Comparison Matrices by Nelder-Mead Algorithm

Pairwise comparison matrices play a prominent role in multiple-criteria decision-making, particularly in the analytic hierarchy process (AHP). Another form of preference modeling, called an incomplete pairwise comparison matrix, is considered when one or more elements are missing. In this paper, an algorithm is proposed for the optimal completion of an incomplete matrix. Our intention is to numerically minimize a maximum eigenvalue function, which is difficult to write explicitly in terms of variables, subject to interval constraints. Numerical simulations are carried out in order to examine the performance of the algorithm. The results of our simulations show that the proposed algorithm has the ability to solve the minimization of the constrained eigenvalue problem. We provided illustrative examples to show the simplex procedures obtained by the proposed algorithm, and how well it fills in the given incomplete matrices.

A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems

The marine predators optimization algorithm (MPA) is a recently developed nature-inspired algorithm. In this paper, the Nelder-Mead algorithm is utilized to improve the local exploitation powers of the MPA when described as a hybrid marine predators and Nelder-Mead (HMPANM). Due to the harsh competitive conditions as well as the transition to new vehicles such as hybrid and full-electrical cars, the interest in the design of light and low-cost vehicles is increasing. In this study, a recent metaheuristic addition, a hybrid marine predators optimization algorithm, is used to solve a structural design optimization problem to prove how the HMPANM can be used in solving industrial design problems. The results strongly prove the capability of the HMPANM for the optimum design of components in the automotive industry.

Comparison of Flux-Switching and Interior Permanent Magnet Synchronous Generators for Direct-Driven Wind Applications Based on Nelder–Mead Optimal Designing

The permanent magnet flux-switching machine (PMFSM) is one of the most promising machines with magnets inserted into the stator. To determine in which applications the use of PMFSM is promising, it is essential to compare the PMFSM with machines of other types. This study provides a theoretical comparison of the PMFSM with a conventional interior permanent magnet synchronous machine (IPMSM) in the gearless generator of a low-power wind turbine (332 rpm, 51.4 Nm). To provide a fair comparison, both machines are optimized using the Nelder–Mead algorithm. The minimized optimization objectives are the required power of frequency converter, cost of active materials, torque ripple and losses of a generator averaged over the working profile of the wind turbine. In order to reduce the computational time, the substituting profile method is applied. Based on the results of the calculations, the advantages and disadvantages of the considered machines were revealed: the IPMSM has significantly lower losses and higher efficiency than the PMFSM, and the PMFSM requires much less rare-earth magnets and copper and is, therefore, cheaper in mass production.