Objective Function Research Articles

A CAM-UNet for pose determination of supercapacitor cell module assembly based on monocular vision

Purpose This paper aims to solve the problems of low stacking efficiency and long production time in the supercapacitor module assembly process, a stacking system based on monocular vision is proposed, including bracket visual positioning, grasping and stacking, and it is applied in actual production. Design/methodology/approach To enhance the robustness of the workpiece location method and improve the location accuracy, the improved U-Net network and image processing algorithms are used to segment the collected images. In addition, for the extracted feature points, the objective function that can be globally optimized is obtained by parameterizing the rotation matrix to construct a polynomial equation system and, finally, the equation system is solved to obtain the final pose estimation, which could improve the accuracy of workpiece location. Findings The result indicates that the proposed method is successfully performed on the manipulator. Besides, this method can well solve the problem of object reflection on the conveyor belt. The Intersection over Union of the image segmentation of the object is 0.9948, and the Pixel Accuracy is 0.9973, which has a high segmentation accuracy for the image. The error range between the method proposed in this paper and the pose estimation is within 2 mm, and the qualified rate of supercapacitor module stacking products is over 99.8%. Originality/value This paper proposes a method of accurately extracting feature points by integrating an improved U-Net network and image processing and uses the workpiece positioning algorithm of the optimal solution PnP problem algorithm. The calculation results show that the algorithm improves the positioning accuracy of the workpiece, realizes the assembly of stacked supercapacitor modules and is applied in industrial production.

Arithmetic optimization algorithm with three-dimensional chaotic mapping in spherical coordinate system for combined economic emission dispatch problem

The objective of the Combined Economic Emissions Dispatch (CEED) problem is to reduce pollutant emissions by lowering the total cost of generating electricity while complying with all other constraints. The multiple objective functions of the CEED problem may be converted into a single objective by using a price penalty factor. Then it was solved with the Arithmetic Optimization Algorithm (AOA) with three-dimensional chaotic mapping in spherical coordinate system. Firstly, five three-dimensional chaotic mappings in Cartesian coordinate system are embedded in the Mathematical Optimization Accelerator (MOA) and the Mathematical Optimization Probability (MOP) of the original algorithm. Secondly, 15 three-dimensional chaotic mappings in spherical coordinate system are constructed from the values of 5 three-dimensional chaotic mappings in Cartesian coordinate system through the mathematical expressions of mode length, polar angle and azimuth angle in spherical coordinate system. Then through a large number of simulation experiments, five best three-dimensional chaotic mappings in the spherical coordinate system are selected to be embedded into the two parameters (MOA and MOP) of the original algorithm to better balance the algorithm's global and local searching ability. The superiority of the improved algorithm is verified by employing 12 benchmark test functions in CEC2022. Eventually, the improved optimal algorithm (IAOA-r) to solve the power system CEED problem is tested on six generating units with four different loads (150 MW, 175 MW, 200 MW and 225 MW). The results of the improved algorithm are compared and analyzed with the results of the Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Bat Algorithm (BAT), Whale Optimization Algorithm (WOA), Harris Hawk Optimization (HHO), Rat Swarm Optimization (RSO). The results demonstrate that the improved algorithm is capable of obtaining optimal fuel costs and smaller pollution emissions in every test case.

A novel hybrid method for predicting maglev train-induced environmental vibrations combining field measurement, 2.5D FEM modelling and multi-objective optimization

The maglev system has been developed rapidly in recently years since its advantage in mass transportation, ride comfort and energy efficiency, which also caused an increasing concern on the maglev train-induced environmental vibrations. This paper presents a novel hybrid framework for predicting high-speed maglev train-induced environmental vibrations combining field measurement, 2.5D FEM simulation and multi-objective optimization. First, a maglev train-guideway-soil coupled model considering guideway irregularities was proposed to calculate the ground vibration using 2.5D FEM. Then, the guideway irregularity spectrum and soil damping are inverted by the optimization process using the NSGA3 genetic algorithm, in which the differences of measured and calculated vertical vibrations of measuring points are chosen as objective functions. With the inverted guideway irregularity spectrum and soil damping, the ground vibrations were calculated using the 2.5D FEM model and compared with the field measurements. It was found that the inverted PSD spectrum of guideway irregularity generally decreases as the spatial frequency increases, exhibiting a similar trend as the measured ones. The results obtained by the proposed hybrid prediction method agree well with the field measurement in both time domain and frequency domain. The prediction error for the vertical vibration level of all measuring points induced by five maglev train passing are mainly less than 10 %, indicating that the proposed hybrid method provided a reliable and effective method for vibration prediction combining field measurement and numerical simulation.

Variable Dose-Constraints Method for Enhancing Intensity-Modulated Radiation Therapy Treatment Planning

The conventional approach to intensity-modulated radiation therapy treatment planning involves two distinct strategies: optimizing an evaluation function while accounting for dose constraints, and solving feasibility problems using feasibility-seeking projection methods that incorporate inequality constraints. This paper introduces a novel iterative scheme within the framework of continuous dynamical systems, wherein constraint conditions dynamically evolve to enhance the optimization process. The validity of dynamically varying dose constraints is theoretically established through the foundation of continuous-time dynamical systems theory. In particular, we formalize a system of differential equations, with both beam coefficients and dose constraints modeled as state variables. The asymptotic stability of the system’s equilibrium is rigorously proven, ensuring convergence to a solution. In practical terms, we leverage a discretized iteration formula derived from the continuous-time system to achieve rapid computational speed. The mathematical structure of the proposed approach, which directly incorporates dose-volume constraints into the objective function, facilitates significant computational efficiency and solution refinement. The proposed method has an inherent dynamics that approaches more desirable solutions within the set of solutions when the solution to the optimization problem is not an isolated point. This property guarantees the identification of optimal solutions that respect the prescribed dose-volume constraints while enhancing accuracy when such constraints are feasible. By treating dose constraints as variables and concurrently solving the optimization problem with beam coefficients, we can achieve more accurate results when compared with using fixed values for prescribed dose conditions.

Identification and application of dynamic parameters of manipulator based on improved IRLS algorithm

Purpose To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm. Design/methodology/approach First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data. Findings Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently. Originality/value It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

Rapid assessment of load-bearing capacity of asymmetric hulls using the MOPSO-based Smith method

ABSTRACT The Smith method, as prescribed by IACS in the Common Structure Rules (CSR) for assessing the load-bearing capacity has limitations in dealing with asymmetric hulls. To address these limitations, the study proposes a fast approach that employs Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to improve convergence and algorithm formulation. It integrates force equilibrium and force vector equilibrium criteria in MOPSO within the Smith method framework. Numerical calculations for Dow’s 1/3 frigate model and double hull VLCC indicated a good correlation with existing methods, such as those proposed in ISSC 2000 and 2012 reports, while also showing better performance compared to Smith method by IACS-CSR, linear search-based, and PSO-based Smith method in accuracy in predicting the bending moment, reliability in tracing of NA and reducing computational time by a factor of 20. The incorporation of the Sigma function and Pareto dominance criteria ensures the solution diversity and prevents prematureness.

Energy-efficient data routing using neuro-fuzzy based data routing mechanism for IoT-enabled WSNs

This paper proposes a novel Neuro-fuzzy-based Data Routing (NFDR) mechanism for efficient data routing and dynamic cluster formation in Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). The NFDR mechanism incorporates optimal scalability factors computed from past and present network parameter values, acting as an additional buffer factor to sustain nodes within clusters, even with partial satisfaction of network parameter values. The neural network determines cluster formation requirements, while the objective function adjusts according to the updated fuzzy logic of identified cluster members. Super heads are initially selected, and cluster member size is adjusted to sustain maximum data transmission without affecting network parameter thresholds. Simulation results demonstrate that the NFDR mechanism enhances clustering range and cluster members while sustaining evaluation metrics such as 75% energy retention, 20% end-to-end delay reduction, and a 15% reduction in dead nodes, ultimately contributing to the development of efficient and robust IoT-enabled WSNs.

Optimal coordination of directional overcurrent relays: A fast and precise quadratically constrained quadratic programming solution methodology

AbstractNowadays, power system protection is increasingly important because of the growing number of customers and the pressing need for timely fault resolution and relay operations. This paper addresses the non‐linear nature of the objective function in the optimal coordination of directional overcurrent relays (DOCRs) by employing a quadratic Taylor series expansion around an operating point, converting the problem into a quadratically constrained quadratic programming problem, ensuring a global optimal solution with increased computational efficiency. Additionally, the quadratic constraints are converted into second‐order cone constraints for compatibility with the CPLEX solver. Using the least square method, the operating point values are determined and further fine‐tuned using iterations with the DOCR operation times. The IEEE 3‐bus, IEEE 8‐bus, and IEEE 14‐bus test systems are used to test the method, which shows higher improvement rates in reducing DOCR operation times and enhancing cooperation than conventional and metaheuristic methods. The simulation results verify the numerical superiority of the method in optimizing the protection system's efficiency while obtaining rapid and accurate solutions. The proposed method was tested on IEEE 3‐bus, 8‐bus, and 14‐bus systems, optimizing relay operating times to 0.87, 2.96, and 7.05 s, respectively, demonstrating the method's efficiency over conventional approaches.

Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.

Symmetric Separable Convex Resource Allocation Problems with Structured Disjoint Interval Bound Constraints

Motivated by the problem of scheduling electric vehicle (EV) charging with a minimum charging threshold in smart distribution grids, we introduce the resource allocation problem (RAP) with a symmetric separable convex objective function and disjoint interval bound constraints. In this RAP, the aim is to allocate an amount of resource over a set of n activities, in which each individual allocation is restricted to a disjoint collection of m intervals. This is a generalization of classic RAPs studied in the literature in which, in contrast, each allocation is only restricted by simple lower and upper bounds, that is, m = 1. We propose an exact algorithm that, for four special cases of the problem, returns an optimal solution in [Formula: see text] time, where the term nF represents the number of flops required for one evaluation of the separable objective function. In particular, the algorithm runs in polynomial time when the number of intervals m is fixed. Moreover, we show how this algorithm can be adapted also to output an optimal solution to the problem with integer variables without increasing its time complexity. Computational experiments demonstrate the practical efficiency of the algorithm for small values of m and, in particular, for solving EV charging problems. History: Accepted by Antonia Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0263 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0263 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Complexity of linearized quadratic penalty for optimization with nonlinear equality constraints

AbstractIn this paper we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints, are locally smooth. For solving this problem, we propose a linearized quadratic penalty method, i.e., we linearize the objective function and the functional constraints in the penalty formulation at the current iterate and add a quadratic regularization, thus yielding a subproblem that is easy to solve, and whose solution is the next iterate. Under a new adaptive regularization parameter choice, we provide convergence guarantees for the iterates of this method to an $$\epsilon $$ ϵ first-order optimal solution in $${\mathcal {O}}({\epsilon ^{-2.5}})$$ O ( ϵ - 2.5 ) iterations. Finally, we show that when the problem data satisfy Kurdyka–Lojasiewicz property, e.g., are semialgebraic, the whole sequence generated by the proposed algorithm converges and we derive improved local convergence rates depending on the KL parameter. We validate the theory and the performance of the proposed algorithm by numerically comparing it with some existing methods from the literature.

Research on Multi-objective Optimization Torque Distribution Strategy for Distributed Drive Electric Vehicles Based on Dung Beetle Optimizer

Abstract Distributed drive electric vehicles (DDEVs), characterized by compact structure, efficient transmission, and flexible control, have gradually become the mainstream in the development of new energy electric vehicles. This study focuses on DDEVs and employs a hierarchical control strategy. At the upper level, a sliding mode controller is used for vehicle yaw stability control, while torque distribution is performed at the lower level. Addressing the shortcomings of conventional average and load-based distribution methods in terms of energy consumption, this paper proposes a multi-objective torque distribution strategy that optimizes tire load ratio, torque variation rate, and motor energy consumption. The strategy integrates objective functions using weighting coefficients and imposes constraints based on road adhesion and motor output capabilities. To tackle this optimization problem, the Dung Beetle Optimizer (DBO) algorithm is introduced, known for its efficient global search capabilities and adaptability. By applying the DBO algorithm, the optimal torque distribution scheme under constraints is determined. Finally, through joint simulations using Carsim and Matlab/Simulink, comparative experiments are conducted under different conditions to evaluate the simulation results of average distribution, load-based distribution, and DBO multi-objective optimization distribution. The experimental findings demonstrate that the proposed multi-objective torque distribution strategy effectively balances tire load ratio, torque variation rate, and motor energy consumption, thereby enhancing the overall performance of distributed drive electric vehicles.

Optimization schemes on manifolds for structured matrices with fixed eigenvalues

AbstractSeveral manifold optimization schemes are presented and analyzed for solving a specialized inverse structured symmetric matrix problem with prescribed spectrum. Some entries in the desired matrix are assigned in advance and cannot be altered. The rest of the entries are free, some of them preferably away from zero. The reconstructed matrix must satisfy these requirements and its eigenvalues must be the given ones. This inverse eigenvalue problem is related to the problem of determining the graph, with weights on the undirected edges, of the matrix associated with its sparse pattern. Our optimization schemes are based on considering the eigenvector matrix as the only unknown and iteratively moving on the manifold of orthogonal matrices, forcing the additional structural requirements through a change of variables and a convenient differentiable objective function in the space of square matrices. We propose Riemannian gradient-type methods combined with two different well-known retractions, and with two well-known constrained optimization strategies: penalization and augmented Lagrangian. We also present a block alternating technique that takes advantage of a proper separation of variables. Convergence properties of the penalty alternating approach are established. Finally, we present initial numerical results to demonstrate the effectiveness of our proposals.

Optimal DSSC’s deployment in power system using PSO

Owing to the high cost of installation and operation, distributed flexible AC transmission system (FACTS) technology gives opportunity to provide cost-effective solution in power system operation and control. A distributed static series compensator (DSSC) is a series FACTS device and it is placed equidistantly placed on the existing line to vary the line current. Active power can be controlled in the line with the help of DSSC. This paper presents DSSC for active power flow control to enhance system loadability index and to minimize reactive power generation by generators. Since DSSC is of low power. a small value of reactance is emulated in the line. Large number of DSSC’s are distributed along the transmission line at regular intervals to realize considerable change in the current. A particle swarm optimization is implemented to determine DSSC’s emulated reactance optimally. In this condition, all the lines flowing power in their limits with increased loading condition. Maximum system loadability index is evaluated by employing optimal number of DSSC’s on the line. A multiobjective problem is formulated. One objective function is formed such that no line would become overloaded even when loading is increased. Other objective is formulated to minimize reactive power generation by generators. A compromise solution is investigated for optimally connected of DSSC devices to achieve both the objective functions. IEEE 14 bus system is taken for MATLAB simulation of DSSC compensated system.

Efficient standardization of clinical T2-weighted images: Phase-conjugacy e-CAMP with projected gradient descent.

To standardize T -weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T maps with the goal of removing scanner- and/or protocol-specific heterogeneity. The T map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD). The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T maps compared to the strict real-value enforcement. It is worth noting that the algorithm works well with challenging T w-TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error. The novel methodological development creates an efficient algorithm that allows for T map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T maps from TSE data with typical clinical parameters has not been previously reported.

A Novel Approach to Node Coverage Enhancement in Wireless Sensor Networks Using Walrus Optimization Algorithm

Wireless Sensor Networks (WSNs) are crucial components of modern technology, supporting applications like healthcare, industrial automation, and environmental monitoring. This research aims to design intelligent and adaptive sensor networks by integrating metaheuristics with node coverage optimization in WSNs. By incorporating metaheuristics and optimizing node coverage, WSNs can become more resilient and robust, leading to the development of self-adapting, self-organizing networks capable of efficiently covering dynamic and diverse environments. This research introduces the Walrus Optimization Algorithm for Node Coverage Enhancement in WSNs, called the WaOA-NCEWSN technique. The primary goal of this technique is to optimize the coverage of a target region using a limited number of Sensor Nodes (SNs) and by improving their placement. The WaOA is inspired by walrus behaviours like feeding, migrating, breeding, escaping, roosting, and gathering in response to environmental signals. The WaOA-NCEWSN technique uses an objective function that defines the coverage ratio, representing the maximum probability of coverage in a 2D-WSN monitoring area. Comparative analysis with other models using 50, 75, 100, and 200 nodes shows that the WaOA-NCEWSN technique performs better. The compilation times for the WaOA-NCEWSN technique are 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively. Experimental results indicate that the WaOA-NCEWSN technique offers superior coverage performance compared to other models.

Optimal design of solar collector network in novel hybrid desalination plant

In this study, a new hybrid system for producing fresh water from seawater using solar energy is proposed. It suggests a modified and optimized approach to reduce the reliance on fossil fuels and decrease pollutants by also harnessing solar energy. The system includes flat-plate solar collectors, a multi-effect distillation through thermal vapor compression (MED-TVC) desalination unit, a pump, and a boiler. The flow rate of the produced fresh water and the specific cost of generating fresh water volume were considered objective functions and the system was optimized using as evolutionary algorithm. The algorithm was employed to determine 12 design variables. Series, parallel, and series-parallel configurations were examined as layouts for flat plate collectors. The results indicated that the series-parallel arrangement was the most effective layout for the collectors. The modeling was carried out for the city of Bandar Abbas in Hormozgan Province, Iran. The production of fresh water in series-parallel mode has increased by 12.15% and 27.83%, respectively, compared to the parallel and series configurations at the ideal point. An assessment of the results suggests that choosing the design point within the flow rate range of 4.19 L/s to 4.65 L/s is preferable.

A Game Theory Based Scheduling Approach for Charging Coordination of Multiple Electric Vehicles Aggregators in Smart Cities

In this paper, a game theory-based coordination approach is presented to find the minimum operation cost of multiple electric vehicle (EV) aggregators that are connected to the upstream network. In order to provide a comprehensive study various EVs aggregators including residential, commercial, official, university, and industry parking lots have been considered and the proposed game theory is utilized to coordinate the charging power from/to different microgrids and/or upstream network. In addition of operation cost, a reliability index, service charge and power loss are included in the objective function. The proposed approach is applied on an electricity network with 7 connected microgrids and different scenarios have been investigated. The simulation results show the overall operation cost in the coalition operation mode is globally minimized in comparison with the non-coalition operation mode. In addition, the reliability index, service charge and power loss force the aggregators to buy their necessary power from the nearby microgrids. The average network loss in the coalition and non-coalition modes are 2.88% and 4.28%, respectively. Moreover, the average reliability cost in coalition and non-coalition modes are $2.65 and $4.25, respectively.

Time-lapse amplitude variation with offset difference inversion based on the reformulated Biot-Squirt model

Time-lapse AVO (Amplitude variation with offset) inversion is a significant technique for the estimation of dynamic reservoir changes. The main theoretical foundation is Zoeppritz equations in single-phase media. At present, there are studies on the inversion method of using Biot's theory. Although it has some improvements in accuracy compared with the method of using Zoeppritz equations, the squirt-flow mechanism is not considered. Furthermore, the precision of difference data equations still needs to be improved in difference inversion. In view of the above problems, the reformulated BISQ (Biot-Squirt) model is introduced into the inversion, and the iterative optimization is used to increase the precision of difference data equations continually in this paper. First, we derive the difference data equations about the reservoir-parameter changes. The reformulated BISQ model is applied, and can describe the characteristics of P-wave reflection accurately and expediently. Secondly, the objective function about the reservoir-parameter changes is constructed. The Bayesian theory and the model smoothing constraint are used, and are helpful to obtain accurate and stable solutions. Thirdly, we derive the equation for estimating the reservoir-parameter changes, and the inversion is iterative for the continuous improvement of accuracy. The difference inversion method is applied, and is helpful to enhance the data repeatability and reduce the calculation amount. Finally, synthetic and real data are applied for the inversion and testing to prove the effectiveness and feasibility of the method.

Label Deconvolution for Node Representation Learning on Large-Scale Attributed Graphs Against Learning Bias.

Node representation learning on attributed graphs-whose nodes are associated with rich attributes (e.g., texts and protein sequences)-plays a crucial role in many important downstream tasks. To encode the attributes and graph structures simultaneously, recent studies integrate pre-trained models with graph neural networks (GNNs), where pre-trained models serve as node encoders (NEs) to encode the attributes. As jointly training large NEs and GNNs on large-scale graphs suffers from severe scalability issues, many methods propose to train NEs and GNNs separately. Consequently, they do not take feature convolutions in GNNs into consideration in the training phase of NEs, leading to a significant learning bias relative to the joint training. To address this challenge, we propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias. More importantly, we show that LD converges to the optimal objective function values by the joint training under mild assumptions. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph Benchmark datasets.