Maximization Of Objective Function Research Articles

A New Integer Model for Selecting Students at Higher Education Institutions: Preparatory Classes of Engineers as Case Study

This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the fair selection of students while respecting criteria such as academic qualifications, required skills, and student preferences. The goal is to develop a decision support system that efficiently selects qualified students at higher education institutions within a reasonable time. The model was tested using real data from Moroccan preparatory classes, achieving important assignment rates across all student categories. Results demonstrate significance in execution time, fulfillment of student choices, and prioritization of outstanding students. This approach offers a flexible, efficient solution for managing academic merit-based selections, optimizing resource utilization, and enhancing fairness in the selection process.

An improved container-based deep forest model for predicting groundwater recharge

Abstract This paper proposes ICDF, an improved container-based deep forest model, for effectively modeling and predicting groundwater recharge. The model consists of four points: the construction and expansion of the container module, the assignment of weights to the base model, the growth of the cascade layer, and the decision output. First, container modules are created and a maximization objective function is assigned to each container to control its growth. Next, different weights are assigned to each base model based on its contribution to container prediction. Cascade layers are built using container modules until the model prediction decreases. Finally, the average prediction vectors of the last cascade layer are output. The model’s performance is evaluated and compared with DF and its base models (random forest, adaptive boosting, and extreme gradient boosting) using a case study of 1549 bores in New South Wales, Australia. Remarkably, compared to DF, ICDF has improved prediction accuracy by 6.66%. Moreover, it outperformed the RF, AdaBoost, and XGBoost by 2.94%, 5.85%, and 5.3% in prediction performance, respectively. The ICDF exhibited superior capabilities in predicting groundwater recharge, offering significant improvement over existing models. Practitioners are encouraged to consider adopting ICDF for groundwater management.

Research on the development of AloT sensing module for new energy power information collection

Abstract In order to improve the ability of power communication technology to support large-scale and massive services of smart electricity consumption, this paper designs a sensing model to collect information on new energy electricity consumption. Using the arrival rate calculation method of intelligent IoT, the information on new energy electricity users is collected, and the environmental measurement data is collected with the help of intelligent sensors. Monitoring the concurrency factor of all sensors in the operation state, collecting queuing theory packet loss rate through utilization maximization objective function, and setting the average queuing length of communication nodes. On this basis, the voltage amplitude of the electrical energy sampling point is measured, and the Gaussian perceptual loss vector with non-zero mean and heteroskedasticity is performed to iteratively converge the constraint matrix and error covariance. The results show that the maximum relative errors for service delay and packet loss rate are 16.8% and 7.0%, respectively. As bandwidth increases, the queuing delay and packet loss rate decrease. The broadband power line carrier can reach up to 210 Mbps, and the micropower wireless rate can reach 38.5 kbps. The annual cycle characteristics fluctuate widely, but the cumulative error of electricity load prediction results is lower, and the prediction accuracy is high. It shows that the electricity consumption information collection sensing model provides an important reference for building a new communication network with low cost, high speed and reliability for smart electricity consumption.

Optimal decision-making in the water, land and food nexus using artificial intelligence and extreme machine learning

Abstract The development of decision-making systems based on artificial intelligence can lead to achieving optimal solutions water-land-food nexus. In this paper, an extreme learning machine model was developed with the objective function of wheat production maximization. The constraints defined for this problem are divided into three categories: technical parameters of production in agriculture, climatic stress on water resources and land limits. The water, land and food nexus was simulated using 23 experimental farms in Henan province during the 2021–2022 cultivation year. Root-mean-square error was used as an error criterion, and Pearson's coefficient was incorporated into the decision-making system as a correlation index of variables. Harvest index, length of the growth period, cultivation costs and irrigation water were the criteria to evaluate the impact of the sustainable model. The harvest index and the length of the growth period showed the highest and lowest correlation with the production rate, respectively. Furthermore, the optimal management of irrigation water and cost had the most significant impact on increasing crop production. The method proposed in this paper can be a virtual cropping model by changing the area under cultivation of a crop in the different farms of a study area, which increases yield production.

MultiNet: Deep unsupervised power control for industrial MU-MIMO networks

This paper presents Multinet, an unsupervised deep learning (DL) approach for power allocation in industrial environments and IIoT applications. Multinet extends the previously proposed singular value decomposition network (SVDNet), which utilizes supervised DL to approximate the performance of the WMMSE algorithm. While SVDNet requires labeled data for training, limiting its scalability and generalization performance, in contrast, Multinet employs unsupervised DL to directly optimize the sum rate maximization objective function, eliminating the need for labeled datasets and improving training efficiency. Simulation studies are conducted to evaluate Multinet’s performance in an industrial environment, utilizing parameters derived from measured large-scale fading characteristics of the industrial radio channel at 5200 MHz. The suitability of Multinet for industrial applications is thus assessed and numerical evaluations demonstrate that Multinet outperforms benchmark supervised and unsupervised DL-based power control schemes in terms of sum rate and energy efficiency.

The solution to the Profit maximization transportation problem using new transportation algorithm

Any engineering system requires engineers to make numerous managerial and technological decisions during the design, building, and maintenance phases. All such decisions aim to either maximize the desired benefit or minimize the amount of effort needed. There is no solitary approach that can effectively address all optimization problems. As a result, number of optimization techniques have been created to address various optimization problems. Transportation problem (TP) formulation is the most significant programing applications in optimization which is attached to everyday life and used in industries under logistics. This study proposed a new algorithm to obtain a basic feasible solution (BFS) for the maximization TP. The approach outlined in this study yields an initial solution that is close to or optimal in most scenarios. A variety of numerical examples are used to demonstrate the new technique. The proposed technique is effective for analyzing balanced or unbalanced transportation problems with maximization objective function.

Firefly optimized robust, imperceptible, secure watermarking scheme

A multi-objective optimized hybrid image watermarking technique is being proposed considering robustness, imperceptibility and security aspects using two different scaling factors. In this technique, original image is subjected to third level lifting wavelet transform (LWT) followed by singular value decomposition (SVD). Watermark is split into two parts to embed each of them into a different subband. In the suggested scheme, firefly algorithm is employed to get optimum solutions for two scaling factors to balance trade-off amid invisibility and robustness. Security in digitized data is an important aspect of image processing. It is improved with a key, an input to Arnold transform for scrambling watermark, to watermark embedding and extraction procedures. All the performance parameters like peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation coefficient (NCC) and bit error rate (BER) are used in formulating maximization objective function. Evaluation of the proposed algorithm indicates that it is characterized by fairly good robustness, invisibility and security.

An energy efficiency perceptive on MIMO‐OFDM systems using hybrid fruit fly‐based salp swarm optimization technique

AbstractThis paper is to plan for the development of energy‐efficient MIMO‐OFDM mobile multimedia communication systems using the intelligent meta‐heuristic approach. This model is concentrated on solving the multi‐channel optimization problem. Recent research work is focused on single‐channel optimization problems, and hence the conversion of multi‐channel optimization problems into single‐channel optimization problems is considered the challenging point in this research. In the proposed MIMO‐OFDM method for mobile multimedia communication systems, the channel‐matrix Singular Value Decomposition (SVD) method is used for implementing the sub‐channel grouping scheme, which has simplified the multichannel problem to a multi‐target single‐channel optimization problem. In this optimization problem, the transmission power‐allocation threshold is optimized for attaining the objective function of maximization of energy efficiency. A hybridized optimization algorithm named Hybrid Fruit Fly‐based Salp Swarm Optimization (HFF‐SSO) is utilized for energy optimization in the MIMO‐OFDM mobile multimedia communication system. This hybrid algorithm has reached an energy‐efficient system with a high convergence rate, which is proved by the comparison over the conventional systems.

A smart IoT-enabled heart disease monitoring system using meta-heuristic-based Fuzzy-LSTM model

A continuous heart disease monitoring system is one of the significant applications specified by the Internet of Things (IoT). This goal might be achieved by combining sophisticated expert systems with extensive healthcare data on heart diseases. Several machine learning-based methods have recently been proven for predicting and diagnosing cardiac illness. However, these algorithms are unable to manage high-dimensional information due to the lack of a smart framework that can combine several sources to anticipate cardiac illness. The Fuzzy-Long Short Term Memory (LSTM) model is used in this work to present a unique IoT-enabled heart disease prediction method. The benchmark data for the experiment came from public sources and collected via wearable IoT devices. An improved Harris Hawks Optimization (HHO) called Population and Fitness-based HHO (PF-HHO) is utilized to select the best features, with the objective function of correlation maximization within the same class and correlation minimization among different classes. The scientific contributions of the health care monitoring system are depicted here that help to improve heart disease healthcare efficiency and also it can be reducing the death rate in the current world. The important section of this persistent healthcare mode is the real-world monitoring system. The simulation outcomes proved that the recommended approach is more successful at predicting heart illness than existing technologies.

Assessing the economic and energy efficiency for multi-energy virtual power plants in regulated markets: A case study in Egypt

• Multiple configurations of VPP aggregating CCHP and Solar PV are assessed. • New profit maximization objective function is presented for regulated markets • Optimization is solved with GA simulating different system sizes. • The propsoed method improved the cost of energy and reduced grid dependency This paper investigates the design and operation management of VPPs in regulated markets. A new framework based on profit maximization objective function is presented in this study. The hypotheses of this research is that considering profit as an objective function would yield a more realistic and optimal sizes compared to Cost of Energy (COE) minimization approach adopted in literature. The analyzed VPP aggregates solar PV units, CCHP supplying power and thermal energy, Battery storage system and thermal energy storage system. The system is formulated in an optimization model fed by energy demand profile, prices and inputs for solar power (irradiance and weather data). The objective function is formulated based on maximization of profit of the VPP selling power to the grid by Power Purchase Agreement (PPA), selling power to consumers at the public electricity tariff, and selling thermal energy at an assumed constant tariff. CCHP non-linear part-load efficiency is also considered in the model, accordingly, Genetic Algorithm (GA) is employed to solve the optimization. Results of the optimally configured model achieved 36% improvement in COE compared to literature. Solar power contributed by 31% from the total produced energy without imbalance, grid power contributed by 4%, and CO2 emissions reduced by 47% compared to full dependency on the grid. Statistical relationships were drawn showing the relationship between profit, energy and exergy efficiencies versus different CCHP capacities. In addition, analysis is provided for the efficiencies’ relation with the dumped heat from the CCHP.

Subspace-based outlier detection using linear programming and heuristic techniques

A useful strategy to perform outlier detection (OD) in highdimensional data, especially in the presence of multiple classes of outliers, is to decompose the outlier detection problem into a set of relevant subspace selection (RSS) problems. In this approach, one relevant subspace is selected locally for each data point, and then the outlierness of that data point within its relevant subspaces is evaluated. The k-nearest neighbor algorithm has been used in most local-based subspace selection methods for OD application in high dimensional data. However, such techniques suffer from the curse of dimensionality, exhibit high computational complexity, and also, it may not be suitable for arbitrary distributions of the data. Additionally, the subspace nearest neighbor search (SNNS) problem in high dimensional data is rarely discussed in these methods. In this paper, we formulate the local selection of relevant subspace method for OD problem that employs a linear programming (LP) model with the objective function of local sparsity maximization. Our formulation for local selection of relevant subspace as an LP also considers the subspace nearest neighbor search problem to simultaneously solve these two subproblems (local selection of relevant subspace and subspace nearest neighbor search) that are tightly coupled. In addition, a novel heuristic algorithm, with linear complexity with respect to the number of dimensions, is developed to solve the LP model through an iterative procedure that alternates between two algorithms of subspace nearest neighbor search and RSS. Experimental results on both synthetic and real datasets show the feasibility of our formulation, the effectiveness of the heuristic algorithm, and also show the superiority of our proposed subspace outlier detection algorithm based on linear programming and heuristic techniques (SODLPH) to other published studies in terms of detection accuracy. Assessing by the average of the AUC on seven real-world UCI datasets, the performance of our proposed method has improved at least 8.72% in comparison with other related published works.

A novel inversion method of 2D TD-NMR signals based on realizing unconstrained maximization of objective function

The inversion of time-domain nuclear magnetic resonance (TD-NMR) signals is an ill-posed problem, which presents enormous challenges for the inversion algorithm. We propose a novel inversion method that converts conventional minimum objective function with non-negative constraints into an unconstrained maximization problem in the inversion of TD-NMR signals. Hence, the objective function becomes a differentiable concave function that can be solved more easily. The validity of the proposed method was verified by the uncertainty estimation of NMR inversion spectra with different signal-to-noise ratios (SNR). Through the inversion of simulated 2D D-T2 and T1-T2 signals under different SNR, the proposed method was proved to be less sensitive to noise than the conventional inversion method. We use the proposed method to study the migrations of oil and water in shales, the components change in shale could be identified and quantified according to the 2D T1-T2 inversion spectra. The proposed method was also used to analyze the hydration process of cement. The 2D T1-T2 inversion spectra could distinctly present the component of tiny volume with short relaxation time, and the migration regularity of capillary water, gel water, and bound water could also be found. In conclusion, the proposed method could be a reliable method to invert TD-NMR signals, especially the identification of the 2D NMR signals with a short relaxation time in low SNR.

A novel threshold score based multiclass segmentation technique for brain magnetic resonance images using adaptive opposition slime mold algorithm

AbstractClinicians can detect diseases early, thanks to the digital image processing methodologies, which improve health together with the treatment experience. The technology of magnetic resonance imaging (MRI) is frequently employed in the brain, research for any kind of related illness. The brain MR image requires precise automated thresholding for a meaningful representation to aid doctors, because of its different modalities and complexity. The majority of the threshold selection strategies are based on entropy. However, these strategies are limited by their reliance on the spatial distribution of gray values. There is also a pressing need to develop a thresholding technique that is independent of the spatial distribution, making it more suitable for a variety of modalities and complexity, such as the brain MR images. A novel non‐entropic maximizing objective function for the multilevel thresholding approach using a threshold score (TS) is presented in this paper, to address these concerns. An evolutionary TS‐AOSMA approach, using the optimizer called adaptive opposition slime mold algorithm (AOSMA), is suggested to lower the computational cost of TS‐based multiclass segmentation, which is a novel idea. The proposed approach is evaluated on T2‐weighted brain MR imaging slices from Harvard Medical School's whole brain atlas dataset. When compared to the state‐of‐the‐art Kapur's, Tsallis, and Masi entropy‐based technologies, the proposed scheme offered better quantitative and qualitative outcomes. The recommended strategies may be useful in medical image analysis.

A New Technique for Finding the Optimal Solution to Assignment Problems with Maximization Objective Function

The assignment problems (AP) are an important part of linear programming problems (LPP) that deal with the allocation of different resources for different activities based on one to one. The assignment problem is established in a variety positions when decision makers need to determine the optimal allocation and this means assigning only one task to one person to achieve maximum profits or imports or achieve less time or less cost based on the type of problem. In this work, a new technique has been provided to find an optimal solution for the assignment problems of maximization objective function. Comparing the proposed technique results with the Hungarian method indicates that the new technique has easier and less steps to find the optimal solution and thus the time is reduced and the effort is largely reduced.

M♮-Convexity and Its Applications in Operations

A new approach for structural analysis of operations models with substitutability structures. In many operations models with substitutability structures, one often ends up with parametric optimization models that maximize submodular objective functions, and it is desirable to derive structural properties including monotone comparative statics of the optimal solutions or preservation of submodularity under the optimization operations. Yet, this task is challenging because the classical and commonly used results in lattice programming, applicable to optimization models with supermodular objective function maximization, do not apply. Using a key concept in discrete convex analysis, M♮-convexity, Chen and Li establish conditions under which the optimal solutions are nonincreasing in the parameters and the preservation property holds for parametric maximization models with submodular objectives, together with the development of several new fundamental properties of M♮-convexity. Their approach is powerful as demonstrated by applications in a classical multiproduct stochastic inventory model and a portfolio contract model.

Fit of the two-component extreme value (TCEV) distribution through of maximum likelihood

En muchas cuencas medianas y grandes de nuestro país y del mundo, su registro anual de crecientes está integrado por eventos que fueron generados por fenómenos físicamente diferentes. Por ejemplo, muchas crecientes se originan con tormentas locales y una porción pequeña es generada por lluvias ciclónicas, de amplia cobertura y duración, que generan crecientes extraordinarias. Para este tipo de registros fue propuesta la distribución TCEV (two–component extreme value) con cuatro parámetros de ajuste, la cual tiene una base teórica que permite una interpretación aproximada para dos mecanismos de generación de las crecientes y que además es capaz de reproducir la variabilidad real del coeficiente de asimetría. En este trabajo se detalla su génesis y el método de ajuste por máxima verosimilitud, según dos versiones numéricas: (1) sustitución sucesiva y (2) maximización de la función objetivo. Se procesaron seis registros de crecientes, cuya amplitud varió de 31 a 72 datos, con tres a seis valores dispersos o crecientes que se apartan de la tendencia general. Se contrastan las predicciones del modelo TCEV, ajustado con cada método numérico, contra las obtenidas con las distribuciones de aplicación establecida bajo precepto (LP3, GVE y LOG) y la Wakeby. Aceptando al error estándar de ajuste como criterio de selección, se deduce que la distribución TCEV es la mejor opción en dos de los seis registros procesados. Por último, se formulan las Conclusiones, las cuales sugieren la aplicación sistemática de la distribución TCEV con ambos métodos numéricos, en los registros que presentan dos poblaciones mezcladas.

Land use optimization through bridging multiobjective optimization and multicriteria decision‐making models (case study: Tilabad Watershed, Golestan Province, Iran)

AbstractThis study aims to present an efficient methodology for land use optimization based on minimization of runoff and sediment and maximization of economic benefits, occupational opportunities, and land use suitability in the Tilabad watershed in northeast of Iran. The land use map of the area was prepared using the Landsat satellite images and field surveys. The amounts of runoff and sediment were estimated via SWAT model. The TOPSIS multicriteria decision‐making (MCDM) approach was applied on the results of the multiobjective optimization (MOO) based on non‐dominated sorting genetic algorithm II (NSGA II) to choose the final optimal solution among the Pareto solutions front generated by MOO. The results indicated that the area of agriculture and rangelands should decrease, and the area of forests should increase to achieve the defined objectives. Overall, results indicated that integration of MOO and MCDM provides an efficient procedure for land use optimization in a complex watershed.Recommendations for Resource Managers The optimization of land use allocation across a complex watershed requires the combined application of several models and techniques to achieve a sustainable decision‐making process. By optimization of land use patterns according to the final solution, the surface runoff and sediment load of the watershed will decline, while the economic profit and land use suitability will improve. Despite using a maximization objective function, the land‐based job opportunities might decrease across a watershed by optimization of land use allocation, but this can be considered as an opportunity for provision of manpower to other socioeconomic sectors.

A multi-year two-stage stochastic programming model for optimal design and operation of residential photovoltaic-battery systems

The residential PV-battery system is becoming economically viable due to technological advances. Selecting optimum investment portfolios and operation strategies are still the challenges faced by homeowners. This study proposed an integrated stochastic sizing-operating framework for optimal design and operation of grid-connected residential photovoltaic (PV)-battery systems. The two-stage stochastic programming method is adopted to address the uncertainties from load and PV productions, where 1) the first stage optimizes the investment portfolio of PV and battery, and 2) the second stage optimizes the energy system operation cost. A multi-year financial model is used to calculate the cash flows during the analysis period. The discounted net present values of expected net benefit (NB) are used as the maximization objective function to make optimal decisions for PV-battery investment and operation. Annual battery degradation factors estimate the aging battery effect. To separately evaluate the PV/battery profitability, we are the first to define the added value/cost metrics of PV/battery. Simulations based on realistic load/PV output profiles and estimated technical–economic parameters validated the effectiveness of the proposed framework and the added value/cost metrics of PV/battery components. The impacts of critical factors, including feed-in tariffs (FiT), tariff profiles and levels, and unit costs, were investigated to provide key findings to stakeholders. This framework can guide the homeowners to cost-effectively invest in and operate the distributed residential PV-battery system to facilitate the transition to a reliable, affordable, and clean building energy system.

Dynamic Pricing Strategy of Electric Vehicle Aggregators Based on DDPG Reinforcement Learning Algorithm

The fixed service charge pricing model adopted by traditional electric vehicle aggregators (EVAs) is difficult to effectively guide the demand side resources to respond to the power market price signal. At the same time, real-time pricing strategy can flexibly reflect the situation of market supply and demand, shift the charging load of electric vehicles (EVs), reduce the negative impact of disorderly charging on the stable operation of power systems, and fully tap the economic potential of EVA participating in the power market. Based on the historical behavior data of EVs, this paper considers various market factors such as peak-valley time-of-use tariff, demand-side response mode and deviation balance of spot market to formulate the objective function of EVA comprehensive revenue maximization and establishes a quarter-hourly vehicle-to-grid (V2G) dynamic time-sharing pricing model based on deep deterministic policy gradient (DDPG) reinforcement learning algorithm. The EVA yield difference between peak-valley time-of-use tariff and hourly pricing strategy under the same algorithm is compared through the case studies. The results show that the scheme with higher pricing frequency can guide the charging behavior of users more effectively, tap the economic potential of power market to a greater extent, and calm the load fluctuation of power grid.

The impact of water-pricing policies on water productivity: Evidence of agriculture sector in Iran

Water pricing and reducing the costs of the implementation and maintenance of irrigation systems have been a challenging topic for a long time. Water pricing issues combine two complex dimensions. The first dimension is the economy of the farm and its association with the broader economic system and farming practices. The second dimension is the hydrology and interconnection of the plot with the irrigation system, the river basin, and the underlying water policy framework. Accordingly, this study aimed to investigate how the institutionalization of water applied would affect water productivity through the implementation of a water-pricing policy. To this end, this study formulated a Positive Mathematical Programming (PMP) model with a gross margin maximizing objective function to assess the data collected from 213 farms in Hamadan-Bahar Plain, Iran. The results revealed that a water-pricing policy can change the cropping pattern and also irrigation system within the limitations of the case study. It can also be a driver to motivate farmers to use a modern and more efficient irrigation system. The potential environmental benefits from the improved on-farm irrigation efficiency depend on the government's ability to prevent the extension of the irrigated area. In absence of such controls, the aquifer depletion will be accelerated. Therefore, an appropriate water-pricing policy could improve water productivity and also reduce water applied.