Simultaneous Optimization Of Parameters Research Articles

Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model

Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge.

Diabetic Retinopathy Detection and Grading: A Transfer Learning Approach Using Simultaneous Parameter Optimization and Feature-Weighted ECOC Ensemble

Diabetic Retinopathy Detection and Grading: A Transfer Learning Approach Using Simultaneous Parameter Optimization and Feature-Weighted ECOC Ensemble

Hydrological Drivers for the Spatial Distribution of Wetland Herbaceous Communities in Poyang Lake

Hydrological processes are known as major driving forces in structuring wetland plant communities, but the specific relationships are not always well understood. The recent dry conditions of Poyang Lake (i.e., the largest freshwater lake in China) are having a profound impact on its wetland vegetation, leading to the degradation of the entire wetland ecosystem. We developed an integrated framework to quantitatively investigate the relationship between the spatial distribution of major wetland herbaceous communities and the hydrological regimes of Poyang Lake. First, the wetland herbaceous community classification was built using a support-vector machine and simultaneous parameter optimization, achieving an overall accuracy of over 98%. Secondly, based on the inundation conditions since 2000, four hydrological drivers of the spatial distribution of these communities were evaluated by canonical correspondence analysis. Finally, the hydrological niches of the communities were quantified by Gaussian regression and quantile methods. The results show that there were significant interspecific differences in terms of the hydrological niche. For example, Carex cinerascens Ass was the most adaptable to inundation, while Triarrhena lutarioriparia + Phragmites australis Ass was the least. Our integrated analytical framework can contribute to hydrological management to better maintain the wetland plant community structure in the Poyang Lake area.

Simultaneous parametric optimization of micro-EDM drilling of brass C360 using Taguchi based grey relation analysis

Micro-EDM (Electro Discharge Machining) is widely used for precision manufacturing and drilling micro-holes in materials used in fuel nozzles, micro sensors and robots, surgical instruments, turbines etc. Present paper deals with drilling common C360 brass components by micro-machining and optimizing the process parameter using a hybrid approach of design of experiments (DoE) and grey relation analysis (GRA) concept. The three process responses considered are material removal rate (MRR), tool wear rate (TWR) and taper in drilled hole while the four selected controllable process parameters are capacitance(C), voltage(V), feed rate(f) and rotational speed of electrode (N). Significance of input factors on each response is analyzed with the help of a 24 full factorial by conducting 16 experiments at two levels for factors. Optimum levels of robustness for each process response are established through Taguchi’s S/N ratio. A combined grey relation grade is applied to find the process levels conducive for optimizing MRR and TWR simultaneously.

Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization

High investment and operating costs are two barriers hindering the commercial deployment of solvent-based post combustion CO2 capture (PCC) technology in power industry. Since upstream power plants are always in flexible operation, conventional approaches which carry out the design based on a fixed condition fail to achieve the best overall performance. To this end, this paper proposes a simultaneous size and operation parameters optimization strategy for the PCC process, in which the flexible operation of the CFPP is taken into account during the design stage. The technique of particle swarm optimization (PSO) is used in a hierarchical manner to find the design and operating conditions of the PCC with minimal total annualized cost. To further explore the optimization potential, the average CO2 capture level in one day is taken as the environmental constraint rather than the conventional instantaneous one. The proposed approach is tested on the PCC design for an existing 660 MW supercritical coal-fired power plant. The optimization results of four different design approaches are compared with the economic, efficiency and environmental (3E) performance being fully assessed. The simulation results show that by using the proposed optimization method, 14.45% reduction in TAC can be attained compared with the conventional approach.

Symbiotic versus nonsymbiotic optimization for spatial and temporal degrees of freedom in pair creation.

The field-induced decay of the quantum vacuum state associated with the creation of electron-positron pairs can be caused independently by either multiphoton transitions or by tunneling processes. The first mechanism is usually induced by appropriate temporal variations of the external field while the second (Schwinger-like) process occurs if a static but spatially dependent electric field is of supercritical strength. The ultimate goal is to construct an optimal space-time profile of an electromagnetic field that can maximize the creation of particle pairs. The simultaneous optimization of parameters that characterize the spatial and temporal features of both fields suggests that the optimal two-field configuration can be remarkably similar to that predicted from two independent optimizations for the spatial and temporal fields separately.

Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm

AbstractImprovement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for a coupled sea ice–ocean model based on a genetic algorithm. To take the interrelation of dynamic and thermodynamic model parameters into account, the system is set up to optimize 15 model parameters simultaneously. The optimization is minimizing a cost function composed of the model–observation misfit of three sea ice quantities (concentration, drift, and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990–2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013–16). The similarity of the final values of the cost function and the resulting sea ice fields from a set of 11 independent optimizations suggest that the obtained sea ice fields are close to the best possible achievable by the current model setup, which allows us to identify limitations of the model formulation. The optimized parameters are applied for a simulation with a higher-resolution model to examine a portability of the parameters. The result shows good portability, while at the same time, it shows the importance of the oceanic conditions for the portability.

Robust Optimum Design of Multiple Tuned Mass Dampers for Vibration Control in Buildings Subjected to Seismic Excitation

Passive energy devices are well known due to their performance for vibration control in buildings subjected to dynamic excitations. Tuned mass damper (TMD) is one of the oldest passive devices, and it has been very much used for vibration control in buildings around the world. However, the best parameters in terms of stiffness and damping and the best position of the TMD to be installed in the structure are an area that has been studied in recent years, seeking optimal designs of such device for attenuation of structural dynamic response. Thus, in this work, a new methodology for simultaneous optimization of parameters and positions of multiple tuned mass dampers (MTMDs) in buildings subjected to earthquakes is proposed. It is important to highlight that the proposed optimization methodology considers uncertainties present in the structural parameters, in the dynamic load, and also in the MTMD design with the aim of obtaining a robust design; that is, a MTMD design that is not sensitive to the variations of the parameters involved in the dynamic behavior of the structure. For illustration purposes, the proposed methodology is applied in a 10‐story building, confirming its effectiveness. Thus, it is believed that the proposed methodology can be used as a promising tool for MTMD design.

Modelling and Optimisation of Coconut Shell Drying and Carbonisation Using Multi-response Taguchi Method with Multi-response Signal-to-noise Procedure

The main purpose of this work is to optimize the biomass drying and carbonization process in terms of both proximate analysis and biomass calorific value, simultaneously. The biomass material used in the study is coconut shells. The independent variables are the drying temperature, the drying time, the carbonization temperature and the carbonization holding time. The dependent variables are proximate analysis and calorific value. The primary methods used to gain the expected result are Taguchi and multi-response signal-to-noise (MRSN) procedure. Simultaneous optimization by using MRSN generate a value of 2.48 and the result corresponds to a drying temperature of 100 °C, a drying time of 24 h, carbonization temperature of 650 °C, and carbonization time of 120 mins. These results are best achieved by using configuration A1B3C3D3. The optimal expected values obtained in this study are maximum calorific values and fixed carbon of 7744 cal/g and 92.934%, respectively, and minimum moisture, volatile matter, and ash content of 0.354%, 2.318%, and 1.437%, respectively. All indicators are satisfied since the resulting model is deemed to be valid and feasible. The novelty of this work is the simultaneous parameter optimization of the five response variables, which have different quality characteristics into a single best parameter. هدف اصلی از این کار این است برای بهینه سازی خشک کردن زیست توده و فرایند کربن از نظر هر دو آنالیز تقریبی و ارزش زیست توده گرمایی، به طور همزمان. مواد زیست توده مورد استفاده در مطالعه پوسته نارگیل است. متغیرهای مستقل هستند که درجه حرارت خشک کردن، زمان خشک شدن، درجه حرارت کربن و کربنی برگزاری زمانی. متغیرهای وابسته هستند آنالیز تقریبی و ارزش گرمایی. روش اصلی استفاده می شود برای به دست آوردن نتیجه مورد انتظار هستند تاگوچی و چند پاسخ سیگنال به نویز (MRSN) روش. بهینه سازی به طور همزمان با استفاده از MRSN تولید یک ارزش 2.48 و نتیجه مربوط به درجه حرارت خشک کردن از 100 درجه سانتیگراد، زمان خشک کردن 24 ساعت، درجه حرارت کربناتی در 650 درجه سانتیگراد و زمان کربناتی در 120 دقیقه. این نتایج با استفاده از بهترین A1B3C3D3 پیکربندی به دست آورد. ارزش مطلوب به دست آمده در این مطالعه مقادیر حداکثر گرمایی و کربن ثابت 7744 کال / g و 92.934٪، به ترتیب، و حداقل رطوبت، مواد فرار، و خاکستر 0.354٪، 2.318٪ و 1.437 درصد، به ترتیب می باشد. تمام شاخص راضی از نتیجه مدل معتبر و عملی محسوب می شود. به تازگی از این کار بهینه سازی پارامتر همزمان از پنج متغیر پاسخ، که دارای ویژگی های با کیفیت مختلف را به یک بهترین پارامتر است.

QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

Modelling and Optimization the Best Parameters of Rice Husk Drying and Carbonization Using Taguchi Method with Multi Response Signal to Noise Procedure

The goal of this study is to optimize parameters the rice husk drying and carbonization process in terms a calorific value and proximate analysis simultaneously. The independent variables are the drying temperature (A) at 100; 105, or 110 °C; the drying time (B) at 12, 18, or 24 h; the carbonization temperature (C) at 450; 550, or 650 °C; and the carbonization holding time (D) at 60; 90, or 120 mins. The methods of the research are Taguchi and multi-response signal-to-noise(MRSN) procedure. The results show that simultaneous optimization of the drying and carbonization of rice husk in terms of the proximate analysis and calorific value, is determined by using the MRSN procedure, and it yields a result of 2.042. The best parameters are a drying temperature of 100 °C; a drying time of 24 h; a carbonization temperature of 650 °C; and a carbonization holding time of 120 mins. The optimal estimated calorific value, fixed carbon, moisture, volatile matter, and ash content are 5665 cal/g, 83.2, 0.06, 3.1 and 6.86 %, respectively. The resulting models are valid and feasible. The advantages of this work compared to previous studies are models and the simultaneous parameter optimization of multi-response variables, which have different quality characteristics into a single best parameter.

Resolving structural errors in a spatially distributed hydrologic model using ensemble Kalman filter state updates

Abstract. In hydrological modeling, model structures are developed in an iterative cycle as more and different types of measurements become available and our understanding of the hillslope or watershed improves. However, with increasing complexity of the model, it becomes more and more difficult to detect which parts of the model are deficient, or which processes should also be incorporated into the model during the next development step. In this study, we first compare two methods (the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) and the Simultaneous parameter Optimization and Data Assimilation algorithm (SODA)) to calibrate a purposely deficient 3-D hillslope-scale model to error-free, artificially generated measurements. We use a multi-objective approach based on distributed pressure head at the soil–bedrock interface and hillslope-scale discharge and water balance. For these idealized circumstances, SODA's usefulness as a diagnostic methodology is demonstrated by its ability to identify the timing and location of processes that are missing in the model. We show that SODA's state updates provide information that could readily be incorporated into an improved model structure, and that this type of information cannot be gained from parameter estimation methods such as SCEM-UA. We then expand on the SODA result by performing yet another calibration, in which we investigate whether SODA's state updating patterns are still capable of providing insight into model structure deficiencies when there are fewer measurements, which are moreover subject to measurement noise. We conclude that SODA can help guide the discussion between experimentalists and modelers by providing accurate and detailed information on how to improve spatially distributed hydrologic models.

Intelligent Medical Disease Diagnosis Using Improved Hybrid Genetic Algorithm - Multilayer Perceptron Network

An improved genetic algorithm procedure is introduced in this work based on the theory of the most highly fit parents (both male and female) are most likely to produce healthiest offspring. It avoids the destruction of near optimal information and promotes further search around the potential region by encouraging the exchange of highly important information among the fittest solution. A novel crossover technique called Segmented Multi-chromosome Crossover is also introduced. It maintains the information contained in gene segments and allows offspring to inherit information from multiple parent chromosomes. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of multi-layer perceptron network in medical disease diagnosis. Compared to the previous works, the average accuracy of the proposed algorithm is the best among all algorithms for diabetes and heart dataset, and the second best for cancer dataset.

A Systematic Approach for Development of an OPLS-Like Force Field and Its Application to Hydrofluorocarbons

A systematic, formal approach to optimization of force field parameters for molecular simulations is presented. The procedure is based on response surface mapping methodology that allows simultaneous parameter optimization against multiple property targets while constraining the number of required computationally expensive numerical experiments. The approach was implemented for prediction of vapor-liquid equilibrium properties of alkanes, alkenes, and their fluorinated derivatives via Monte Carlo molecular simulations. To further reduce computational costs, a bootstrap procedure that involves a sequence of parameter optimization for four pairs of compounds (ethane and propane, ethene and propene, perfluoroethane and perfluoropropane, and 2,3,3,3-tetrafluoropropene and (E)-1,3,3,3-tetrafluoropropene) was used. The results of simulations utilizing the optimized force field parameters agree well with associated reference equations of state.

An Algorithm for Simultaneous Optimization of Parameters of Condition-based Preventive Maintenance

A new condition-based preventive maintenance model for a system, subject to deterioration-failures and to random failures with increasing intensity, is presented. Deterioration is modeled as discrete stages. After an inspection, based on the degree of deterioration, a minimal maintenance or a major maintenance is performed, or no action is taken. Deterioration failures are restored by complete replacements; Random failures are restored by minimal repair. Major maintenance restores the system to `τ' deterioration stages younger (τ > 1), while minimal maintenance restores the system one stage. The proposed model considers an accumulated deterioration based increasing intensity for the random failures. A continuously increasing failure rate (for example Weibull) is converted into a stepwise increasing failure rate using stair-step approximation. An exact recursive algorithm computes the steady-state probabilities of the system. Based on maximization of the system availability or minimization of total cost, an optimal inspection policy within this strategy, optimal inter-inspection time, and threshold deterioration level for replacement, are obtained. The effects of threshold deterioration level for deterioration replacements, inspection, maintenance, and repair parameters are investigated.

A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks

The back-propagation network (BPN) can be used in various fields. Nevertheless, different problems may require different parameter settings for network architectures. Rule of thumb or ''trial and error'' methods are usually used to determine them. However, these methods may lead worse parameter settings for network architectures. On the other hand, although a dataset may contain many features, not all features are beneficial for classification in BPN. Therefore, a simulated-annealing-based approach, denoted as SA+BPN, is proposed to obtain the optimal parameter settings for network architectures of BPN, and to select the beneficial subset of features which result in a better classification. In order to evaluate the proposed SA+BPN approach, datasets in UCI Machine Learning Repository are used to evaluate the performance of the proposed approach. The experimental results show that the parameter settings for network architectures obtained by the proposed approach are better than those of other approaches. When the feature selection is taken into consideration, the classification accuracy rates of most datasets are increased. Therefore, the developed approach can be utilized to find out the optimal parameter settings for network architectures of BPN, and discover the useful features effectively.

Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme

Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models.

Optimization of solar cell performance

A new technique for optimizing silicon solar cell performances has been established. The parameters to be optimized are replaced by independent variables without constraints. The variables include diffusion and recombination parameters, as well as non ideal effects such as band gap narrowing. The method allows a simultaneous optimization of parameters leading to maximization of solar cell efficiency and open-circuit voltage. It suggests an allowable range of the efficient parameters, and a new cell design criterion in terms of a thin base with a low recombination velocity.