Numerical Optimization Approach Research Articles

A Sampling-Based Approach for Achieving Desired Patterns of Probabilistic Coverage with Distributed Sensor Networks.

A new method is derived for finding the best positions in which to locate the sensors in a distributed sensor network in order to achieve a desired variation, or pattern, in spatial coverage over a specified domain. Such patterning is important in situations when there are not enough sensors to completely cover a region adequately. By providing coverage based on a desired pattern, this approach allows a user/designer to specify which sub-regions of the domain are more important to cover, and to what level that is desired. The method that is developed is novel in that it is an analytic approach, as opposed to existing numerical optimization approaches, and thus provides solutions rapidly and can also be applied to provide online repositioning for existing sensor networks to respond to changes in the environment. The method is based on deriving an expression for the probabilistic density of sensor locations that best matches the desired coverage under given spatially varying environmental conditions; and then samples from that sensor density to determine specific sensor locations. The performance of the method is demonstrated on numerical examples in both one-dimensional and two-dimensional settings. Comparisons are made between solutions found from this approach and solutions obtained by a numerical optimization technique.

Efficient GaN-on-Si Power Amplifier Design Using Analytical and Numerical Optimization Methods for 24–30 GHz 5G Applications

This paper presents the design procedure of an efficient compact monolithic microwave integrated circuit power amplifier (MMIC PA) in a 0.1 μm GaN-on-Si process for 5G millimeter-wave communication. Load/source-pull simulations were conducted to correctly create equivalent large-signal matching models for stabilized power cells and to determine the optimal impedance domain. The shorted stub with bypass capacitors minimizes the transistor’s output reactance, simplifying the matching objective to an approximate real impedance transformation (IT). With miniaturization as the implementation guide, explicit formulas and tabulated methods based on mathematical analysis were applied to synthesize the filtering matching networks (MNs) for the input and output stages. In addition, a CAD-dependent numerical optimization approach was used for the interstage MN that needs to cope with high IT ratio and complex loads. The continuous-wave (CW) characterization for the proposed two-stage PA demonstrated 19.8 ± 0.7 dB of small-signal gain, very flat output power (Pout) and power-added efficiency (PAE) at 4 dB gain compression of 32–32.4 dBm and 34–34.6%, respectively, over 24–30 GHz, with 37.1% of peak PAE at mid-frequency.

A review of experimental and numerical analysis on laser beam machining of composites and thermoplastics

Material cutting is regarded as a crucial process owing to its strategic value in the production of items such as aircraft, ships, automobiles, biomedical components, etc. Laser beam cutting is among the efficient techniques for creating complicated geometries with rigorous design criteria. This article discusses the experimental study of the laser cutting process and the effect of laser cutting parameters on composites and thermoplastics. A number of numerical modelling and different optimization approaches employed by various researchers is also reviewed in this article.

Model reduction for the Chemical Master Equation: An information-theoretic approach.

The complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist's toolkit. For stochastic reaction networks described using the Chemical Master Equation, commonly used methods include time-scale separation, Linear Mapping Approximation, and state-space lumping. Despite the success of these techniques, they appear to be rather disparate, and at present, no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper, we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimizing a well-known information-theoretic quantity between the full model and its reduction, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimization approaches. In addition, we derive general expressions for propensities of a reduced system that generalize those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop, the Michaelis-Menten enzyme system, and a genetic oscillator.

A numerical optimization approach for pricing components in customer defined bundles in a B2B market

This research examines the problem of pricing products in multi-product, multi-quantity bundles in a business-to-business market setting in which customers’ valuation of products diminishes with the size of the bundle. The framework developed in this paper consists of three steps: (i) estimation of diminished valuation of each product based on their stand-alone valuations and bundle composition, (ii) maximization of expected profit of the firm to obtain the optimal bundle price, and (iii) maximization of customer satisfaction to obtain the optimal split of the bundle price into component prices. A Nelder–Mead optimization based method is proposed as a solution approach, which leverages an importance sampling based Monte Carlo integration method to approximate the quantities of interests in the model. The framework is also used to analyze the behavior of the model for quantity increase. Results indicate that the price per unit of each product decreases when the quantity of any of the products is increased. However, the rate of decrement is the least for the product whose quantity is incremented. Additional results show that fixing prices (and not re-optimizing), when the bundle composition changes, can significantly reduce the firm’s profit.

Analysis of Optimal Sensor Placement in Looped Water Distribution Networks Using Different Water Quality Models

Urban looped water distribution systems are highly vulnerable to water quality issues. They could be subject to contamination events (accidental or deliberate), compromising the water quality inside them and causing damage to the users’ health. An efficient monitoring system must be developed to prevent this, supported by a suitable model for assessing water quality. Currently, several studies use advective–reactive models to analyse water quality, neglecting diffusive transport, which is claimed to be irrelevant in turbulent flows. Although this may be true in simple systems, such as linear transport pipes, the presence of laminar flows in looped systems may be significant, especially at night and in the peripheral parts of the network. In this paper, a numerical optimisation approach has been compared with the results of an experimental campaign using three different numerical models as inputs (EPANET advective model, the AZRED model in which diffusion–dispersion equations have been implemented, and a new diffusive–dispersive model in dynamic conditions using the random walk method, EPANET-DD). The optimisation problem was formulated using the Monte Carlo method. The results demonstrated a significant difference in sensor placement based on the numerical model.

AniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation

Abstract Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. State–space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error‐prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time‐consuming to fit to diverse animal tracking data sets. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: (1) a simple but extensible workflow that accommodates both novice and experienced users; (2) automated processes that alleviate complexity from data processing and model specification/fitting steps; (3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. We highlight aniMotum's capabilities through three applications to real animal tracking data. Full R code for these and additional applications is included as Supporting Information, so users can gain a deeper understanding of how to use aniMotum for their own analyses.

Microgrid control for renewable energy sources based on deep reinforcement learning and numerical optimization approaches

Optimal scheduling of battery energy storage system plays crucial part in dis- tributed energy system. As a data driven method, deep reinforcement learning does not require system knowledge of dynamic system, present optimal solution for nonlinear optimization problem. In this research, financial cost of energy con- sumption reduced by scheduling battery energy using deep reinforcement learning method (RL). Reinforcement learning can adapt to equipment parameter changes and noise in the data, while mixed-integer linear programming (MILP) requires high accuracy in forecasting power generation and demand, accurate equipment parameters to achieve good performance, and high computational cost for large- scale industrial applications. Based on this, it can be assumed that deep RL based solution is capable of outperform classic deterministic optimization model MILP. This study compares four state-of-the-art RL algorithms for the battery power plant control problem: PPO, A2C, SAC, TD3. According to the simulation results, TD3 shows the best results, outperforming MILP by 5% in cost savings, and the time to solve the problem is reduced by about a factor of three.

Developing the optimization control of pollution constrained by the convection diffusion equations

In this paper, an optimization control of pollution subject to the convection diffusion equations is developed and analyzed. The optimization objective function is to consider the pollution effects for minimizing the weighted derivation between simulated concentrations and allowable concentrations at observation sites and the costs of emission reductions, pollution taxes, and the cost related to layout of pipelines. The proposed optimal control problem is defined on the multiple observation and polluting points and is constrained by the convection diffusion equations. We analyze this optimization problem from a rigorous mathematical viewpoint and prove the existence of a solution of the pollution optimal control. We build the numerical optimal control approach by a second-order improved splitting upwind finite volume method to solve the convection diffusion equations and by the constrained difference evolution (DE) algorithm to compute the optimization procedure. Three numerical experiments are investigated, where the first one aims at searching the optimal disposal fluxes while minimizing concentration deviation and reduction cost, the other two are to minimize the whole economical costs that contain waste water treatment cost, pipeline layout cost and pollution taxation by finding the optimal effluent concentration of waste water. The developed optimal control is efficient and it can be applied in applications.

Оптимизация проточной части осевого насоса с использованием прямых методов

The direct methods were developed for optimizing the OP-5 axial pump flow section based on the DesignXplorer tool algorithms of the ANSYS Workbench software package. At the first stage, the MOGA genetic algorithm and the ASO gradient method were used. To increase the optimization efficiency, the input parameters were correlated. Accuracy of the relationship between the input and output parameters was evaluated by the determination coefficients values and the scatter diagram. Such studies made it possible to reduce the number of input parameters from nineteen to seven. It is shown that the best approach for numerical optimization of the axial pump flow section is the consistent use of stochastic and local methods.

Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning

Differential evolution (DE) has recently attracted a lot of attention as a simple and powerful numerical optimization approach for solving various real-world applications. However, the performance of DE is significantly influenced by the configuration of control parameters and mutation strategy. To address this issue, we proposed reinforcement learning-based hybrid parameters and mutation strategies differential evolution (RL-HPSDE) in this paper. The RL-HPSDE is based on the Q-learning framework, the population individual is regarded as an agent. The optimization problem's dynamic fitness landscape analysis results are utilized to represent the environmental states. The ensemble of parameters and mutation strategy is employed as optional actions for the agent. Furthermore, a reward function is designed to guide the agent to perform the optimal action strategy. Based on its reinforcement learning experience stored by the corresponding Q table, the agent could adaptively select an optimal combination of mutation strategy and parameters to generate offspring individual during each generation. The proposed algorithm is evaluated using the CEC2017 single objective test function set. Several well-known DE variants are also compared with the proposed algorithm. Empirical studies suggest that the proposed RL-HPSDE algorithm is competitive with all other competitors.

CHITOSAN NANOBUBBLES DEVELOPMENT AND EVALUATION FOR THE DELIVERY OF SUNITINIB-AN ANTICANCER AGENT

Objective: In the current study, we introduced a novel method for creating Sunitinib nanobubbles by incorporating it into chitosan-shelled nanobubbles. Methods: The Design Expert® programme randomly assigned around 13 experiments, and multiple regression analysis was used to statistically examine the data. The effect of the amount of sunitinib, amount of chitosan, amount of Epikuron 200, amount of palmitic acid and stirring speed, on percent encapsulation efficiency and drug load while maintain minimum particle size of nanobubbles as considered through a definitive screening plan. By placing limitations on the response parameters, the optimum formulation was created using a numerical optimization approach. The three improved formulations (Batch1 through Batch3) were assessed. Results: The findings show that the nanobubbles particle size of 78.56-82.42 nm with an encapsulation efficiency of 68.48-69.56 % and loading capacity of 23.88-25.02%. The quantity of sunitinib released from nanobubbles was much larger (96.52 percent) than that from the sunitinib solution within 24 h, according to an in vitro release profile of the medication using ultrasonography. The hemolytic activity of the blank nanobubbles and sunitinib-loaded nanobubbles was measured to assess their safety up to a concentration of 10 mg/ml. With erythrocytes, drug-loaded nanobubbles had a good safety profile. FTIR, DSC studies indicated no chemical interactions, TEM images revealed nanobubbles size of 70-100 nm and stability studies shows no significant changes. Conclusion: For contrast-enhanced tumour imaging and subsequent therapeutic administration, nanobubbles were found to be superior.

Numerical optimization approach for the development of Giloy stem (Tinospora cordifolia) extract based functional yoghurt

AbstractFunctional yoghurt is a prominent fermented milk product. It is popular among all age groups and has considerable significance due to the presence of active nutrients to fulfill the human dietary needs. Tinospora cordifolia (giloy) is extensively discussed during the recent pandemic owing to its high antioxidant, anti‐inflammatory, and strong immunity boosting properties. This investigation has been aimed to optimize functional yoghurt impregnated with ultrasound assisted giloy stem extract. Central composite rotatable design (CCRD) was used for experimental design of process parameters (giloy stem extract, total soluble solid, and incubation temperature). Functional yoghurt formulation was optimized based on different (whiteness index, overall acceptability, pH, syneresis, firmness, DPPH scavenging activity, and complex viscosity) responses. The results revealed that giloy stem extract and total soluble solid exhibited maximum effects on all the responses. Numerical optimization revealed the optimum combination of giloy stem extract, total soluble solid, and incubation temperature as 0.08%, 9.1%, and 45°C, respectively. At this optimum condition, the whiteness index, overall acceptability (OA), pH, syneresis, firmness, DPPH scavenging activity, and complex viscosity values of optimized functional yoghurt were 84.513, 8.567, 4.193, 47.268%, 281.19 N, 56.78%, and 76.89 cP, respectively. This study established the effect of giloy extract addition, total soluble solids, and incubation temperature on yoghurt preparation and subsequent changes in its properties.Novelty impact statement Giloy stem extract (GSE) enriched functional yoghurt was developed having high antioxidant activities. Ultrasound assisted GSE improved the texture and overall desirability of functional yoghurt. Derived the optimum conditions which might be replicated and scaled up for pilot scale production.

Modeling and Optimization of Mass and Heat Flux Profiles in a Multifunctional Reactor for CO2 and H2 Valorization to Dimethyl Ether

Dimethyl ether is one of the future synthetic fuels in the 2025–2050 energy mix. This work presents a detailed numerical optimization approach for a multifunctional reactor for the direct synthesis of dimethyl ether from CO2 and H2, with a specific focus on the methodological aspect that can be used as a guide to help or evaluate the development of innovative technological strategies. An innovative approach based on the dynamic optimization of the mass and heat flux profiles is applied for the intensified configuration, coupling simultaneously the reaction, heat exchange, and membrane separation (R–E–M). Several degrees of freedom and optimization strategies (mono- and multi-objective/deterministic and stochastic) are addressed. The energetic criterion is evaluated, namely, the total heat power exchanged. Optimization results highlight the performance improvement potentialities offered through the optimal exploitation of the synergy between these elementary functions. Simulation results show that the kinetic inhibition caused by water can be overcome and that a water-free process output is achievable, enabling an economic gain due the removal of a postreaction separation (water/methanol). The heat flux optimization provides more flexibility for mass transfer. Up to 95 and 98% in DME yield and CO2 conversion have been obtained per single pass for optimal theoretical heat- and mass-transfer flux profiles, respectively, within a multifunctional reactor. The results are obtained for the reactor inlet temperature and pressure of 518.15 K and 70 bar, respectively.

Mutivariate optimization strategy for the sonication-based extraction of Nardostachys jatamansi roots and analysis for chemical composition, anti-oxidant and acetylcholinesterase inhibitory potential

Extracts from medicinal plants are generally obtained by conventional methods like percolation and maceration. Owing to limitations of traditional methods and to meet the rising demand of extracts, the development of new green approaches is need of hour. In the present research, we have developed an ultrasound-assisted extraction (UAE) method for the Nardostachys jatamansi (NJ) D. Don, DC roots and optimized the extraction parameters for possible improved extract yield. A multivariate optimization strategy using the Centre Composite Design coupled with response surface methodology was applied. A numerical optimization approach accurately predicted the extraction conditions (sonication time ∼ 20 min, ethanol ∼ 70 % and a liquid/solid ratio of about 21:1). Scanning electron microscopy of the plant samples after UAE also indicated the cavitation effect due to sound waves. GC–MS analysis of the optimized ultrasound extract (OUNJ) confirmed improvement in the concentration of various secondary metabolites like jatamansone (91.8 % increase), spirojatamol (42.3 % increase), globulol (130.4 % increase), sitosterol (84.6 % increase) as compared to the soxhlet extract (SXNJ). Different anti-oxidant parameters (DPPH, Glutathione, Catalase SOD and NO) were also significantly altered (p < 0.05) in the optimized extracts. The IC50 to inhibit acetylcholinesterase activity (AChE) in vitro and its concentration in brain homogenates were significantly (p < 0.05) improved by OUNJ extract as compared to the SXNJ ones. To conclude, we can say that established optimized conditions for UAE of N. jatamansi roots not only reduce the extraction time but also improved the pharmacological potential of the extracts.

A numerical optimization approach for structural reliability analysis using the control parameters in the generalized HLRF method

A numerical optimization approach for structural reliability analysis using the control parameters in the generalized HLRF method

Numerical approach for optimization of magnetic roller and evaluating the performance of permanent magnet roller separator through design of experiment

The present study is focused on numerical analysis of magnetic roller (Mr) using finite element method magnetics (FEMM) software for different magnet disc-to-steel disc (MD-to-SD) width ratios. The numerical (FEMM) results reveal that, the optimized Mr with the MD-to-SD width ratio of 5 mm: 2.5 mm was proved advantageous because of the effective magnetic field (Mf) value of 0.89–2.59 T. The artificial neural network (ANN) modelling technique was used for the prediction analysis of obtained numerical results. Furthermore, by using optimized Mr, the lab-scale permanent magnet roller separator (PMRS) was developed and parametric optimization has been carried out using Taguchi-based L27 orthogonal array design. The significance of parameters on the overall quality of the product has also been evaluated quantitatively by the analysis of variance (ANOVA) method. It was found that the belt thickness was the most influential factor in the product of desired Fe grade and recovery %. The obtained regression coefficient (i.e., R2 = 87.13 and 91.69% for Fe grade and Fe recovery %, respectively) and normal probability plot show the highest correlation between the experimented and predicted data. The results suggested that the numerical approach was suitable for designing optimized Mr for the processing of paramagnetic minerals.

Measurement of turbulent supersonic steam jet flow characteristics using TDLAS

Ejectors have no moving parts and are preferable to mechanical compressors in many applications, but ejectors typically have a relatively low efficiency. To aid in the ejector design process, thorough understanding of the turbulent mixing of multi-phase compressible jets is beneficial.This paper reports experimental results for Tunable Diode Laser Absorption Spectroscopy (TDLAS) measurements derived from an axisymmetric supersonic steam jet apparatus.In this experimental work, a supersonic steam jet nozzle exit of a diameter 13.6 mm was surrounded by a low-speed flow of dry nitrogen. The TDLAS system was traversed through the flow at three different planes downstream from the ejector nozzle exit: 15, 20, and 30 mm distance. At each of the three planes, line-of-sight measurements were made with the laser passing through locations between 0 and 15 mm from the jet centreline.Through the analysis of the TDLAS data and application of the Abel inversion method, the radial distribution of the pressure, temperature, and the concentration of the water-vapour were obtained. The key findings are that it is possible to determine key physical parameters using experimental TDLAS measurements when combined with a suitable numerical optimization approach.

A modified model parametrization algorithm for solving a special type of heat and mass transfer systems

A new method for solving nonlinear heat and mass transfer design tasks was considered. Systems using the Number of Transfer Units (NTU) method are a special type of mathematical model of heat and mass exchangers. It was observed, that the NTU models in a form of differential-algebraic equations (DAEs) cannot be directly solved with higher values of NTU. The requirements for consistent initial conditions, as well as numerical limitations of DAEs solvers, result, that the solution to the considered design problems that cannot be obtained by a classical direct shooting procedure. To overcome the presented difficulties, the αDAE model optimization algorithm was adjusted for solving NTU-based models. The new approach consists of 3 main steps: 1) task discretization by a multiple-shooting approach, 2) design an appropriate function fNTU(α) to effectively influence the variability of the state variables described by dynamical relations, 3) the iterative numerical optimization algorithm for the new parametrized system. Moreover, computations can be performed by a chosen numerical optimization approach, which can be communicated with an available outer procedure for solving differential-algebraic equations. The presented algorithm was implemented and applied to solve the design task with the NTU model of a counter-flow exchanger. The new approach was used to modify the system dynamics to influence the difficulty of the considered problem. Finally, the presented method enabled failure-free numerical computations for the higher values of the NTU parameter.

Zein-Stabilized Nanospheres as Nanocarriers for Boosting the Aphrodisiac Activity of Icariin: Response Surface Optimization and In Vivo Assessment.

Icariin (ICA), a main active compound of the Epimedium genus, is used as an aphrodisiac in traditional Chinese herbal medicine. Despite its therapeutic efficacy, ICA displays reduced oral absorption, and therefore, low bioavailability hindered its clinical application. Implementing nanotechnology in the field of formulation has been a focus to improve the efficacy of ICA. In this regard, polymeric nanoparticles find a potential application as drug delivery systems. A nanosphere formula was designed, aiming to improve the drug’s efficacy. The proposed ICA nanosphere formula (tocozeinolate) was optimized using D-optimal response surface design. The concentrations of ICA (X1), D-α-tocopherol polyethylene glycol 1000 succinate (TPGS, X2), zein (X3), and sodium deoxycholate (SDC, X4) expressed as percentages were investigated as quantitative independent variables. As per the experimental design, 23 formulations were developed, which were investigated for particle size (PS, nm), zeta potential (ZP, mV), and entrapment efficiency (EE, %) as response parameters. Numerical optimization and desirability approach were employed to predict the optimized variable levels that, upon combination, could result in minimized size and maximized zeta potential and ICA entrapment. The optimized ICA–tocozeinolate nanospheres showed a particle size of 224.45 nm, zeta potential of 0.961 mV, and drug entrapment of 65.29% that coincide well with the predicted values. The optimized ICA–tocozeinolate nanospheres were evaluated for sexual behavior in Wistar male rats compared to raw ICA at equivalent doses (20 mg/kg). In vivo assessment results showed significant sexual behavior enhancement by the optimized formulation, as evidenced by decreased average time of both mount latency (ML) and ejaculation latency (EL) to almost half those of raw ICA. Additionally, intromission latency (IL) time was reduced by 41% compared to the raw ICA. These results highlighted the potential of the proposed ICA–tocozeinolate nanospheres as a promising platform for improving the delivery and efficacy of therapeutic agents.