Objective Function Research Articles

A comparison of the renal function biomarkers serum creatinine, pro-enkephalin and cystatin C to predict clearance of pemetrexed.

For drugs with a narrow therapeutic window, there is a delicate balance between efficacy and toxicity, thus it is pivotal to administer the right dose from the first administration onwards. Exposure of pemetrexed, a cytotoxic drug used in lung cancer treatment, is dictated by kidney function. To facilitate optimized dosing of pemetrexed, accurate prediction of drug clearance is pivotal. Therefore, the aim of this study was to investigate the performance of the kidney function biomarkers serum creatinine, cystatin C and pro-enkephalin in terms of predicting the elimination of pemetrexed. We performed a population pharmacokinetic analysis using a dataset from two clinical trials containing pharmacokinetic data of pemetrexed and measurements of all three biomarkers. A three-compartment model without covariates was fitted to the data and the obtained individual empirical Bayes estimates for pemetrexed clearance were considered the "true" values (Cltrue). Subsequently, the following algorithms were tested as covariates for pemetrexed clearance: the Chronic Kidney Disease Epidemiology Collaboration equation using creatinine (CKD-EPICR), cystatin C (CKD-EPICYS), a combination of both (CKD-EPICR-CYS), pro-enkephalin as an absolute valueor in a combined algorithm with age and serum creatinine, and lastly, a combination of pro-enkephalin with cystatin C. The dataset consisted of 66 subjects with paired observations for all three kidney function biomarkers. Inclusion of CKD-EPICR-CYS as a covariate on pemetrexed clearance resulted in the best model fit, with the largest decrease in objective function (p < 0.00001) and explaining 35% of the total inter-individual variability in clearance. The predictive performance of the model to containing CKD-EPICR-CYS to predict pemetrexed clearance was good with a normalized root mean squared error and mean prediction error of 19.9% and 1.2%, respectively. In conclusion, this study showed that the combined CKD-EPICR-CYS performs best in terms predicting pharmacokinetics of pemetrexed. Despite the hypothesized disadvantages, creatinine remains to be a suitable and readily available marker to predict pemetrexed clearance in clinical practice.

Hybrid extreme gradient boosting regressor models for the multi-objective mixture design optimization of cementitious mixtures incorporating mine tailings as fine aggregates

The design of cementitious mixtures incorporating mine tailings as fine aggregates is a multi-objective optimization (MOO) problem, in which both the uniaxial compressive strength (UCS) and cost of the mixtures need to be considered simultaneously. Given that data-driven methods have shown promising results when solving similar MOO problems, this study developed an extreme gradient boosting regressor (XGBR) model on a dataset extracted from the literature to predict the UCS. Among the efforts taken to improve the models, a genetic algorithm (GA)-based XGBR model demonstrated the optimal prediction performance, with an R2 of 0.959. Next, the GA-XGBR model and a cost equation were used as objective functions in the MOO problem. The non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was selected to solve the optimization problem. A case study was conducted, generating mixture designs that offered improved trade-offs between cost and UCS compared to experimental designs. Finally, a graphical user interface was developed to provide access to the prediction model and optimization method. Overall, this work can be used as a guide for optimal mixture designs as it facilitates informed decision-making before the actual applications.

Mimicking Human Hearing with Topologically Optimized Structure

The remarkable auditory capabilities of human ears have inspired the development of bio-inspired acoustic sensing technologies. In this study, a topology optimization problem is formulated to replicate the hearing profile of the human ear and is solved using a gradient-based optimization method. To ensure manufacturability and mesh independence, a density filter is employed to eliminate gray scale transitions between solid and void regions. The objective function is defined to minimize the error between the human's Head-Related Transfer Function (HRTF) of a reference structure and the designed domain in specific frequencies and directions. Numerical simulations demonstrate that the optimized structure exhibits similar acoustic characteristics and spatial directivity to that of the ideal human ear at the designated frequencies. The proposed structure and optimization method show great potential for applications such as 3D audio rendering, hearing- aid design, and sound source localization.

Design and implementation of low power multiplierless FIR filters on FPGA and ASIC

ABSTRACT This paper presents a low-power design and implementation approach for multiplierless FIR filters by reducing switching activity between filter coefficients using different optimisation algorithms. The objective function simultaneously minimises hamming distance between the consecutive filter coefficients, passband, and stopband ripples. Low-pass, high-pass, band-pass, and band-stop FIR filters of different order and word lengths are designed using improved grey-wolf optimisation. Comparison with grey-wolf optimisation, hybrid particle swarm optimisation, and grey-wolf optimisation, and whale optimisation algorithms is performed for different statistical parameters. The proposed filter is also compared with existing filter designs and shows considerable improvement in design metrics. For low-power implementation of designed FIR filters, carry-save-adder trees are utilised. FPGA and ASIC platforms are used for implementation. Basys-3 (Artix-7) FPGA board is targeted using the Vivado tool for FPGA implementation. Hardware design metrics such as slice LUTs, slice registers, slices, and dynamic power consumption are obtained. ASIC implementation is also performed using the Cadence Genus tool for a 45 nm Nan gate open cell library and total area, power, and delay are reported. The obtained results for FPGA slice utilisation and power consumption provide an average reduction of 61.39% and 74.13%, respectively. ASIC implementation provides an average 58.60% reduction in area-power product.

Trainable signal encoders that are robust against noise

Within the deep learning paradigm, finite impulse response (FIR) filters are often used to encode audio signals, yielding flexible and adaptive feature representations. We show that a stabilization of FIR filterbanks with fixed filter lengths (convolutional layers with 1-D filters) by means of their condition number leads to encoders that are optimally robust against noise and can be inverted with perfect reconstruction by their transposes. To maintain their flexibility as regular neural network layers we implement the stabilization via a computationally efficient regularizing term in the objective function of the learning problem. In this way, the encoder keeps its expressive power and is optimally stable and noise-robust throughout the whole learning procedure. We show in a denoising task where noise is additionally present in the encoder representation of the signals, that the proposed stabilization of the trainable filterbank encodings is decisive for increasing the signal-to-noise ratio of the denoised signals significantly compared to a model with a naively trained encoder.

Traffic microsimulation for smart cities: Investigating the impact of objective function formulation on calibration efficiency

Traffic microsimulation for smart cities: Investigating the impact of objective function formulation on calibration efficiency

When two chains meet: Optimizing the design of blockchain-enabled supply chain networks

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

Optimizing customized bus services for multi‐trip urban passengers: A bi‐objective approach

AbstractCustomized bus services typically focus on single‐trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi‐objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi‐trip requests and time‐dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP‐hard problem, a non‐dominated sorting genetic algorithm II‐based method is proposed to handle different sizes of instances. Finally, the instances with multi‐trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search‐based multi‐objective algorithm approach.

A dual-standby power source configuration scheme for micro grid in Pelagic Island

Realizing clean energy supply is important for the sustainable development of pelagic island. The volatility and randomness features of renewable energy generation may reduce power supply reliability, thus backup units is necessary in micro-grid in pelagic island. This paper proposed a dual-standby configuration scheme based on Sequence Operation Theory to achieve reliable power standby. Firstly, characteristics of standby units, micro gas turbine and diesel generator, are analyzed. Next, the probability distribution function models of wind, photovoltaic and load demand in pelagic island are established, and the reliability index of power shortage probability is calculated by Sequence Operation Theory. Then, the two-stage dual-standby control scheme to make full use of the complementary standby units is introduced and object function to minimize equivalent annual cost on the premise of satisfying probabilistic reliability index is modeled. Hybrid integer particle swarm algorithm is used to solve the problem. Finally, the proposed model for dual-standby configuration is applied to a pelagic island. The simulation results show that the proposed method can meet the load demand in both daily and extreme weather and achieve superior economy.

Design optimization of phononic-fluidic resonator systems

Phononic-fluidic resonators utilize an acoustic fluid domain as a defect inside a solid metamaterial lattice, which can be used used to precisely measure acoustic properties of liquids. The design goal is to offer ideal boundary conditions for the acoustic resonator by matching defect resonance with phononic band gap. The design space of metamaterial or phononic crystal and fluidic resonator is nearly unlimited, especially when utilizing fully three-dimensional structures. In this work, we explore numerical shape and topology optimization to focus acoustic energy into the fluid domain and maximize the quality factor of the defect resonance. This requires the use of complex multi-frequency objective functions, while taking into account practical limits such as finite size and losses in the fluid and solid domains. Results highlight notable but limited improvements of quality factor with shape optimization starting from an initial design constrained by the maximum deformation possible. On the other hand, topology optimization results offer unique and unexpected ideas, and thus a deeper dive into the design space.

Design and optimization of acoustic reflector for uniform diffusion

Sound diffusion performance is considered as an important issue in building acoustics. A well-diffused sound field can effectively enhance the human listening experience. To this end, we have investigated several optimization strategies to design a sub-wavelength acoustic metasurface that can achieve broadband uniform diffuse reflections. These designs are based on Fresnel lens surface, groove-like structure, level-set method, topology optimization approach, respectively. Meanwhile, a strategy combining Monte-Carlo searches and other derivative-free algorithms has been analyzed and used to ensure, as far as possible, a global optimum for the objective function when dealing with the multi-peak problem encountered in complex sound fields. Numerical simulations show that the resulting metasurfaces with the best diffusion performance have similar configurations between different design methods. The simulated sound pressure field was also verified by experimental measurements. From a general perspective, the proposed optimization strategy and simulation framework can provide a greater degree of freedom for geometric configuration and benefit the design of interior components in building acoustics.

Advancements and future directions in single-cell Hi-C based 3D chromatin modeling

Single-cell Hi-C data provides valuable insights into the three-dimensional organization of chromatin within individual cells, yet modeling this data poses significant challenges due to its inherent sparsity and variability. This review comprehensively explores the predominant approaches to reconstructing 3D chromatin structures from single-cell Hi-C data, positioning these methods within the broader contexts of single-cell Hi-C research and bulk Hi-C data modeling.We categorize the modeling strategies based on their objective functions, which are framed in terms of force fields, potentials, cost functions, or likelihood probabilities. Despite their diverse methodologies, these approaches exhibit deep underlying similarities. We further dissect the basic components of these models, such as attractive restraint forces and repulsive forces, and discuss additional terms like fluid viscosity and variation penalties.The review also critically evaluates the current state of model validation, highlighting the inconsistencies across various studies and emphasizing the need for a comprehensive validation framework. We detail common validation techniques, including the comparison of distance matrices and the assessment of contact violations.We argue that the future of single-cell Hi-C modeling lies in integrating multiple data modalities and incorporating cell cycle trajectory information. Such integration could significantly advance our understanding of chromatin conformation dynamics during cell cycle progression and cell differentiation. We also foresee the continued growth of optimization-based and molecular dynamics approaches, supported by general molecular dynamics toolkits.

Machine learning-based rainfall forecasting in real-time optimal operation of urban drainage systems

This paper presents a rainfall forecast-based real-time optimal operation model of urban drainage systems (UDSs) investigating the role of rainfall forecasts in predictive real-time operation of UDSs. Two rainfall forecast models, namely long short-term memory (LSTM) and optimized-by-PSO (particle swarm optimization) support vector regression, are linked to an integrated simulation–optimization model. For any given operation policies, the rainfall-runoff stormwater management model (SWMM) simulates the urban system’s operation under flooding conditions in which the operation policies are optimized by the metaheuristic harmony search (HS) optimizer. The objective function of the HS algorithm is minimizing flood volumes at control points. The presented approach is therefore a dynamic predictive real-time operation (PRTOP) model that continuously updates rainfall forecasts and operational decisions during flood events. The performance of the developed forecast-based integrated SWMM-HS simulation–optimization modeling framework is tested through its application in a real urban system case study located in Tehran, Iran. Results demonstrate that a notable reduction in flood volume (11.8%) is achieved by using the LSTM-driven rainfall forecasts in the proposed PRTOP model compared to a reactive real-time operation (RTOP) model not utilizing rainfall forecasts.

An optimal scheduling method for heavy haul trains with virtual coupling technology

ABSTRACT Heavy haul transportation enjoys the benefits of cost-efficiency by using long trains, but suffers from high cost in combination process and low efficiency in bottleneck areas. Virtual coupling technology (VCT) is helpful in resolving the dilemma. In this paper, we propose an optimal method for scheduling the route, timetable, and combination scheme of heavy haul trains with VCT in an integrated way. Specifically, six groups of constraints are first established, in which the sequence and train length problems incorporated by virtual coupling are properly handled. Then, a combined objective function comprised makespan and total flowtime indicators is proposed to optimize the throughput and turnover rate and further speeds up the optimization convergence. Finally, the optimization problem equipped with the proposed constraints and the combined objective function is transformed into an integer linear programming problem and solved by GUROBI. Simulation results demonstrate the effectiveness and advantages of the proposed method in leveraging VCT, and the benefits of virtual coupling over traditional physical coupling for heavy haul transportation.

Population Pharmacokinetic Model of Intravenous Immunoglobulin in Patients Treated for Various Immune System Disorders

Intravenous immunoglobulin (IVIG) is used to treat various immune system disorders, but the factors influencing its disposition are not well understood. This study aimed to estimate the population pharmacokinetic parameters of IVIG and to investigate the effect of genetic polymorphism of the FCGRT gene encoding the neonatal Fc receptor (FcRn) and clinical variability on the pharmacokinetic properties of IVIG in patients with immune system disorders. Patients were recruited from 4 hospitals in Malaysia. Clinical data were recorded, and blood samples were taken for pharmacokinetic and genetic studies. Population pharmacokinetic parameters were estimated by nonlinear mixed-effects modeling in Monolix. Age, weight, baseline immunoglobulin G concentration, ethnicity, sex, genotype, disease type, and comorbidity were investigated as potential covariates. Models were evaluated using the difference in objective function value, goodness-of-fit plots, visual predictive checks, and bootstrap analysis. A total of 292 blood samples were analyzed from 79 patients. The IVIG concentrations were best described by a 2-compartment model with linear elimination. Weight was found to be an important covariate for volume of distribution in the central compartment (Vc), volume of distribution in the peripheral compartment (Vp), and clearance in the central compartment, whereas disease type was found to be an important covariate for Vp. Goodness-of-fit plots indicated that the model fit the data adequately. Genetic polymorphism of the FCGRT gene encoding the neonatal Fc receptor did not affect the pharmacokinetic properties of IVIG. This study supports the use of dosage based on weight as per current practice. The study findings highlight that Vp is significantly influenced by the type of disease being treated with IVIG. This relationship suggests that different disease types, particularly inflammatory and autoimmune conditions, may alter tissue permeability and fluid distribution due to varying degrees of inflammation. Increased inflammation can lead to enhanced permeability and retention of IVIG in peripheral tissues, reflecting higher Vp values.

Heterogeneity-aware integrative regression for ancestry-specific association studies.

Ancestry-specific proteome-wide association studies (PWAS) based on genetically predicted protein expression can reveal complex disease etiology specific to certain ancestral groups. These studies require ancestry-specific models for protein expression as a function of SNP genotypes. In order to improve protein expression prediction in ancestral populations historically underrepresented in genomic studies, we propose a new penalized maximum likelihood estimator for fitting ancestry-specific joint protein quantitative trait loci models. Our estimator borrows information across ancestral groups, while simultaneously allowing for heterogeneous error variances and regression coefficients. We propose an alternative parameterization of our model that makes the objective function convex and the penalty scale invariant. To improve computational efficiency, we propose an approximate version of our method and study its theoretical properties. Our method provides a substantial improvement in protein expression prediction accuracy in individuals of African ancestry, and in a downstream PWAS analysis, leads to the discovery of multiple associations between protein expression and blood lipid traits in the African ancestry population.

Selecting robust silicon photonic designs after Bayesian optimization without extra simulations

Optimization methods are frequently exploited in the design of silicon photonic devices. In this paper, we demonstrate that pushing the objective function to its minimum during optimization often results in devices that gradually become more sensitive to perturbations of design variables. The dominant strategy of selecting the design with the smallest objective function can lead to fabrication failure or yield loss due to manufacturing process variations. To address this issue, we propose an intuitive selection criterion that can identify designs not only possessing small objective functions but that are also robust to variations. Our simulation results on the Y-splitter, direction coupler, and bent waveguide designs demonstrate that the proposed method can achieve 2x higher coverage of robust designs with almost negligible run time, compared to the two baseline methods.

Optimizing the Activation of WWTP Wet-Weather Operation Using Radar-Based Flow and Volume Forecasting with the Relative Economic Value (REV) Approach

Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results.

Solving discrete network design problem using disjunctive constraints

AbstractThis paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount.

Addressing Fuzzy Linear Programming Problems Through Ranking Functions

In contemporary decision-making, reliance on information is paramount, yet much of it is fraught with uncertainty, making logical decision-making challenging. To tackle this uncertainty, various methods, including the use of fuzzy numbers, have been employed. This paper specifically delves into addressing linear programming (LP) problems characterized by fuzzy coefficients in the objective function, fuzzy values in the right-hand side, and fuzzy coefficients of constraints. The proposed approach involves employing linear ranking functions such as Maleki, Campos, Yager’s F1 and Yager linear ranking functions to solve these fuzzy linear programming (FLP) problems and attain optimal solutions. Furthermore, the paper elucidates the resolution steps through the presentation of numerical examples, in this study, a comprehensive methodology is presented for effectively addressing a wide range of linear programming problems.