Objective Function Research Articles

Automatic positioning of cutting planes for bone tumor resection surgery.

In bone tumor resection surgery, patient-specific cutting guides aid the surgeon in the resection of a precise part of the bone. Despite the use of automation methodologies in surgical guide modeling, to date, the placement of cutting planes is a manual task. This work presents an algorithm for the automatic positioning of cutting planes to reduce healthy bone resected and thus improve post-operative outcomes. The algorithm uses particle swarm optimization to search for the optimal positioning of points defining a cutting surface composed of planes parallel to a surgical approach direction. The quality of a cutting surface is evaluated by an objective function that considers two key variables: the volumes of healthy bone resected and tumor removed. The algorithm was tested on three tumor cases in long bone epiphyses (two tibial, one humeral) with varying plane numbers. Optimal optimization parameters were determined, with varying parameters through iterations providing lower mean and standard deviation of the objective function. Initializing particle swarm optimization with a plausible cutting surface configuration further improved stability and minimized healthy bone resection. Future work is required to reach 3D optimization of the planes positioning, further improving the solution.

Simultaneous Optimization of Hydrogen Network with Pressure Swing Absorption Based on Evolutionary Response Surface Method

The simultaneous optimization of complex process units and hydrogen networks is a significant challenge in refinery hydrogen network integration. To address this, an evolutionary response surface-based collaborative optimization method is proposed, enabling the concurrent optimization of pressure swing adsorption (PSA) and the hydrogen network. This method develops a mechanistic model for PSA and alternates between random sampling and evolutionary response surface-based hydrogen network optimization to obtain diverse sampling points and potential optimal solutions. The PSA mechanistic model is then used to compute the accurate output parameters for the sampled points, and these parameters are incorporated into the hydrogen network optimization to obtain precise objective function values. An efficient optimization framework is presented to streamline the process. The proposed method is applied to a refinery hydrogen network integration case study, comprehensively considering both PSA costs and hydrogen utility costs. The results demonstrate that the method is computationally efficient and effectively reduces the refinery’s total annual costs. The accuracy of the optimization results is significantly improved compared to traditional methods, providing an effective solution for the collaborative optimization of the refinery hydrogen network and PSA.

Leveraging cultural algorithms for FOPID controllers with objective function analysis

Leveraging cultural algorithms for FOPID controllers with objective function analysis

Integrating collision avoidance strategies into multi-robot task allocation for inspection

Recent research topics have been placed on reinforcing security and safety measures within high-risk industries to protect both equipment and the environment. Numerous industries carry substantial implications for their surroundings. During significant incidents like chemical spills, or nuclear accidents, swiftly gathering precise and dynamic data poses a considerable challenge. Subsequently, this paper focuses on optimizing a mission involving multiple mobile robots charged with inspecting an industrial zone. The aim of this research is to efficiently collect measurements from diverse positions using a fleet of sensing robots operating from a central depot, that is, developing an algorithm for robot decision-making that optimizes mission planning by minimizing an objective function. Initially, we explore our previous proposed solutions and we improve the system by integrating a navigational layer to manage collision avoidance between robots. Then, we delve into scenarios involving multiple homogeneous tasks distributed in a limited geographical environment. To demonstrate feasibility, extensive simulations, numerical experiments, and comparative analysis are conducted, showing the efficiency of the proposed approaches in terms of solution quality and computational complexity.

Multiscale design optimization of lattice structures considering dynamic response minimization and reaction force uniformity

A multiscale design optimization model is proposed for a vibrating hierarchical lattice structure, considering minimization of vibration response and uniformity of reaction forces at the fixed boundary. Two different lattice unit cells are hybridized to improve the manufacturability and mechanical properties of the lattice structures. Lattice microstructures are designed and controlled by multiple design variables. Surrogate models are used to fit the equivalent performance with the design variables. An optimization formulation is established for minimizing the displacement response amplitude at specified locations of the structure. The variance and mean value of the reaction force are introduced as constraints for the uniform reaction force at the structure boundary. Sensitivities of the objective and constraint functions are derived with the adjoint method. Finally, the effectiveness of the method is verified by several numerical examples, and the influence of the variance constraint of the reaction force at the structure boundary is discussed.

Efficient Frontier and Applications in Product Offering and Pricing

Problem definition: The joint assortment and pricing problem is notoriously difficult to solve, especially for large-scale issues subject to various operational constraints arising from business practices. In this paper, we delve into the joint optimization challenge under constrained logit choice models, accounting for product-differentiated price sensitivities across static, randomized, and dynamic contexts. Methodology/results: We develop a unified optimization methodology that applies efficient frontier and dimensional reduction and transforms the joint optimization problem into a single-variable one with respect to the total choice probability. The efficient frontier is employed to transform the nonconcave objective function equivalently into its concave counterpart. We show that the optimal prices are uniquely determined by the common target adjusted markup. The complexity of the mixed combinatorial optimization problem can be reduced by searching over efficient sets of polynomial size. In the randomized assortment and pricing problem in which the product offer sets and prices follow certain distribution, we show that randomization has the potential to elevate the total revenue to the exact efficient frontier, a feat that the static problem may fail to achieve (with a given total choice probability). For dynamic joint product selection and pricing, we find that the optimal policy adopts a simple time-threshold structure that can be precomputed. Furthermore, we illustrate the robustness of our methodology by extending the analysis to a variety of general settings, including the nested logit model. Managerial implications: The proposed methodology contributes to the increasingly popular topic in retail management by significantly reducing the computational complexity of joint product offering and pricing under different constraints. Our results regarding the effects of product set constraints and price bounds provide valuable insights and guidance to practitioners on product offering and pricing in various business scenarios. Funding: C. Ke acknowledges financial support from the National Natural Science Foundation of China [Grant 72101113]. L. Lu acknowledges financial support from the Hong Kong Research Grants Council [Grants 26501021 and 16502423]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0164 .

Optimization of a viscous-elastic damper coupling two simple oscillators based on nonlinear stochastic response

Abstract Among the techniques used to control or mitigate the structural vibrations induced by dynamic input, such as wind and earthquake, the dissipative coupling is one of the most applied, especially for its ease of implementation. Indeed, for example in large urban areas, it is common to find adjacent structures where the space between the buildings becomes smaller. To optimally select the visco-elastic features of the dissipative device to be used, the paper retraces the path followed by the previous scientific works proposing new design criteria. Such criteria are based on the nonlinear stochastic response of two simple oscillators linked by a damper whose hysteretic behavior is represented by a Bouc-Wen model. A state-space formulation of the equations of motion has been adopted to facilitate the analysis of the dynamic response. At the same time, the loading is hypothesized as a zero-mean Gaussian excitation. Consequently, the nonlinear response has been approximately evaluated by the equivalent linearized standard deviations for both displacements and accelerations. Subsequently, formulations of objective functions, based on the Minimax and Total Energy of the equivalent linearized stochastic response, have been applied to determine the optimal configurations of the coupled system. The influence of both noise power amplitude and soil typology on the designed systems has been also investigated. Suggestions related to the path to achieve pre-fixed targets (as balancing of displacements and accelerations) are provided.

Optimizing fuzzy power system stabilizers for microgrid frequency stability using improved water cycle algorithm

Abstract This research assignment has incorporated electric vehicles and robust Fuzzy Power System Stabilizer (Fuzzy-PSS) controller for improving stability behavior of an AC microgrid. Load dynamics, wind fluctuation , variations in solar intensity, etc, have a major impact on the performance of an islanded AC microgrid. The anticipated Fuzzy-PSS controller acts as the secondary load frequency control loop of the microgrid and mitigates the frequency oscillation problems in the microgrid. The suggested Fuzzy Power System stabilizer (Fuzzy-PSS) technique is the best bet for addressing system frequency stability in different disturbances. Under various circumstances, the Improved Water Cycle Algorithm (I-WCA) is recommended for tuning the controller gains through a suitable objective function. Regarding microgrid’s frequency regulation, the effectiveness of the ideal Fuzzy PSS controller is contrasted with that of the conventional fuzzy controller and PID controller. The suggested Fuzzy-PSS approach strongly outperforms the standard Fuzzy PID controller in regard to stability improvement of the EV injected AC microgrid. The suggested Fuzzy-PSS technique has the ability to settle ∆F 2 (area 2 microgrid frequency) by 150.56% and 216.72%, respectively, as compared to the standard Fuzzy PID and PID approaches. To demonstrate the superiority of the suggested I-WCA over standard PSO approach, its dynamic performance is examined from an optimization point of view.

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach’s accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

Development of a Service-Oriented Interoperability Framework: The Botswana E-Government System Case Study

Efficient information exchange between government entities and citizens is crucial for effective governmental service delivery. However, e-government systems in developing countries like Botswana face challenges due to a lack of communication and integration among these systems. This case study addresses the interoperability challenges in Botswana's e-government systems by exploring and documenting the development of the e-government service-oriented interoperability framework (e-GSOIF). This framework integrates various technological and methodological approaches to improve service delivery and efficiency. It incorporates service-oriented architecture (SOA), event-driven architecture (EDA), ontologies, a refined software development lifecycle methodology, and the interoperability practical implementation support (IPIS) approach. The study pinpoints key factors impacting e-government system implementation and interoperability in Botswana through interviews, questionnaires, and observation. It also identifies essential technical components for E-Government Interoperability. The e-GSOIF framework is evaluated against the original IPIS framework using exploratory factor analysis and compatibility assessment with predefined functionality criteria, demonstrating its superiority. This paper targets government officials, IT specialists, researchers, and students interested in e-government services interoperability. It offers insights into advancing the landscape of e-government service delivery through an effective interoperability framework.

The optimal control law of the power supplied to the wheeled vehicle in case of curvilinear movement

INTRODUCTION: Highly mobile wheeled vehicles are designed to move on roads and terrain in various road and soil conditions, which is accompanied by frequent and significant changes in traction forces and rolling resistance forces. In this regard, in order to maintain vehicle mobility and ensure low energy costs when performing transport tasks, it is necessary to continuously change the operating mode during movement from completely locked to differential in the case of a mechanical transmission. At the same time, the transmission operating mode selected by the driver is not always rational. Thus, the development of a law for controlling the power supplied to the propulsion system, ensuring minimal energy losses while maintaining vehicle mobility in widely varying road conditions, is an urgent task. PURPOSE OF THE STUDY: Increasing the energy efficiency of highly mobile wheeled vehicles by applying a control law for the power supplied to the propulsion system, adaptive to driving conditions. METHODOLOGY AND METHODS: Increasing the energy efficiency of movement can be achieved by reducing losses due to wheel slipping by controlling the power supplied to the propulsion unit. It is advisable to obtain the control law as a result of solving an optimization problem, where the loss power is chosen as the objective function, and the traction forces developed on each of the wheels are chosen as the varied values. At the same time, in order to maintain the possibility of vehicle movement, it is necessary to take into account that the total traction force on all wheels must be determined by external conditions and provided by the power plant. To solve the optimization problem, the Lagrange multiplier method was used. RESULTS AND SCIENTIFIC NOVELTY: The studies carried out made it possible to obtain in analytical form a unified law of adaptive control of the power supplied to the propulsors, applicable in a wide range of road conditions in both straight and curved motion, providing a close to optimal distribution of moments across the driving wheels of the machine, obtained as a result of numerical optimization. PRACTICAL SIGNIFICANCE: The application of the developed law for controlling the power supplied to the propulsors, based on the use in the process of movement of information only about the longitudinal and vertical forces, rotational speeds and angles of rotation of the wheels (trajectory radius), will improve the efficiency of performing transport tasks when moving a vehicle in continuously changing road conditions due to reducing the load on the driver in terms of controlling differential locks in comparison with a manual transmission both in straight and curved motion.

A novel multi-sensor data fusion enabled health indicator construction and remaining useful life prediction of aero-engine

Remaining useful life (RUL) prediction is vital to formulate a suitable maintenance strategy in manufacturing systems health management. Multisensor data fusion of complex engineering systems has attracted substantial attention due to the fact that a single sensor can only collect partial information. Health indicator (HI) construction plays a crucial role in multisensor data fusion and machinery prognostic, mainly because it attempts to quantify a history and ongoing degradation process by fusing the advantages of multiple sensors. However, large numbers of coefficients are involved for most of the existing HIs. Additionally, simplifications during modeling may inhibit the wide application of the constructed HI. To address these two challenges, a new multisensor data fusion method is proposed in this paper by constructing a HI for the characterization of the degradation process. Firstly, the sensors that collect invalid data or conflicting data are removed through a correlation coefficient operation. Then, principal component analysis (PCA) is adopted to reduce the number of coefficient before constructing the HI. Furthermore, the objective function is constructed under the comprehensive consideration of the three factors of the HI, that is, monotonicity, trendability, and fitting errors. The effectiveness of the proposed method is verified using the C-MAPSS dataset. Multiple comparison results show that the HI possesses excellent performance in both degradation characterization and remaining useful life prediction.

Genome-wide association studies are enriched for interacting genes

BackgroundWith recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.ResultsWe used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.ConclusionsOur findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

ATKB-PID: an adaptive control method for micro tension under complex hot rolling conditions

At present, the parameters of the controllers in hot rolling roughing microtension control systems are not adaptively adjustable to variations in working conditions, which compromises both width accuracy and production stability. To address this issue, this paper introduces an ATKB-PID adaptive micro tension control method. This method incorporates a linear attention layer and utilizes a K-Nearest Neighbors (KNN) algorithm to predict the optimal learning rate and inertia coefficient under actual operating conditions. Furthermore, an objective function is tailored to production indices to enhance model performance. Comparative experiments with both established and recently introduced controllers demonstrate that the proposed ATKB-PID method exhibits a smaller steady-state error and quicker adjustment time. The ATKB-PID control method is well-suited for the complex and dynamic microtension control demands in thermal roughing processes, showing promising application potential.

Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network

The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.

A bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation

With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete and high-dimensional decision variables make the quality of the solution obtained with acceptable time not guaranteed. Second, the desired solution of real missions often needs to satisfy multiple objective functions, or a set of solutions for decision-making. Therefore, this paper constructs a Multi-objective Combinatorial Optimization in Multi-UAV Task Allocation Problem (MCOTAP) model, and proposes a Bi-subpopulation Coevolutionary Immune Algorithm (BCIA). The two coevolutionary mechanisms improve the lower limit of population diversity, and the evolutionary strategy pool integrating multiple strategies and the adaptive strategy selection mechanism enhance the local search ability in the late evolution. In the experiments, BCIA competes fairly with the mainstream multi-objective evolutionary algorithms (MOEAs), multi-objective immune algorithms (MOIAs) and the recently proposed multi-UAV mission planning algorithms. The experimental results on different test problems (including several multi-objective combinatorial optimization benchmark problems and the proposed MCOTAP model) show that BCIA has superior performance in solving multi-objective combinatorial optimization problems (MCOPs). At the same time, the effectiveness of each design component of BCIA has been comprehensively verified in the ablation study.

A new approach for solving the multidimensional control optimization problems with first‐order partial differential equations constraints

AbstractThe purpose of this paper is to provide the linearization technique to solve the multidimensional control optimization problem (MCOP) involving first‐order partial differential equation (PDEs) constraints. Firstly, we use the modified objective function approach for simplifying the aforesaid extremum problem (MCOP) and show that the solution sets of the original control optimization problem and its modified control optimization problem (MCOP) are equivalent under convexity assumptions. Further, we use the absolute value exact penalty function method to transform (MCOP) into a penalized control problem (MCOP) . Then, we establish the equivalence between a minimizer of the modified penalized optimization problem (MCOP) and a saddle point of the Lagrangian defined for the modified optimization problem (MCOP) under appropriate convexity hypotheses. Moreover, the results established in the paper are illustrated by some examples of MCOPs involving first‐order PDEs constraints.

Highly Efficient and achromatic mid-infrared silicon nitride meta-lenses

Inverse design with topology optimization considers a promising methodology for discovering new optimized photonic structure that enables to break the limitations of the forward or the traditional design especially for the meta-structure. This work presents a high efficiency mid infra-red imaging photonics element along mid infra-red wavelengths band starts from 2 to 5 µm based on silicon nitride optimized material structures. The first two designs are broadband focusing and reflective meta-lens under very high numerical aperture condition (NA = 0.9). The two designs are modeled by inverse design with topology optimization problem with Kreisselmeier–Steinhauser (k–s) aggregation objective function, while the final design is depended on novel inverse design optimization problem with double aggregation objective function that can target bifocal points along the wavelength band producing high efficiency achromatic broadband multi-focal meta-lens under very high numerical apertures ().

Many-Objective Truss Structural Optimization Considering Dynamic and Stability Behaviors

The most commonly used objective function in structural optimization is weight minimization. Nodal displacements, compliance, the first natural frequency of vibration, the critical load factor concerning global stability, and others can also be considered additional objective functions. This paper aims to propose seven innovative many-objective structural optimization problems (MOSOPs) applied to 25-, 56-, 72-, 120-, and 582-bar trusses, not yet presented in the literature, in which the main objectives, in addition to the structure’s weight, refer to the structures’ vibrational and stability aspects. These characteristics are essential in designing structural models, such as the natural frequencies of vibration and load factors concerning global stability. Such new MOSOPs have more than three objective functions and are called many-objective structural optimization problems. The chosen objective functions refer to the structure’s weight, the natural frequencies of vibration, the difference between some of the natural frequencies of vibration, the critical load factor concerning the structure’s global stability, and the difference between some of its load factors. The sizing design variables are the cross-sectional areas of the bars (continuous or discrete). The methodology involves the finite element method (FEM) to obtain the objective functions and constraints and multi-objective evolutionary algorithms (MOEAs) based on differential evolution to solve the MOSOPs analyzed in this study. In addition, multi-criteria decision-making (MCDM) is adopted to extract the solutions from the Pareto fronts according to the artificial decision-maker’s (DM) preference scenarios, and the complete data for each chosen solution are provided. For the MOSOP with seven objective functions, it is possible to observe variations in the final weights of the optimum designs, considering the hypothetic scenarios, of 21.09% (25-bar truss), 289.73% (56-bar truss), 70.46% (72-bar truss), 45.35% (120-bar truss), and 74.92% (582-bar truss).

Hierarchical quantitative prediction of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertainty risk

In order to provide a reliable basis for the cost management of photovoltaic power generation, it is necessary to accurately predict the depreciation expense of photovoltaic power generation. Therefore, a hierarchical quantitative prediction method of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertain risks is proposed. Based on the conditional value-at-risk theory, a more comprehensive risk measure than VaR is provided, and the uncertainty risk value of photovoltaic power generation is calculated by considering the average loss exceeding this loss value. According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. In practical application, the test results show that this method can complete the risk quantitative analysis of uncertain factors, and the tracking ability and fitting degree of prediction are good; An ordered list of solutions of each objective function can be generated; The method in this paper is used to predict the depreciation expense of photovoltaic power generation in the first 10 solutions of priority ranking, and the maximum deviation of the prediction result is -0.65 million yuan.