Objective Function Research Articles

P-1236. Vancomycin Population Pharmacokinetics Using Volumetric Absorptive Microsampling in Critically Ill Children with Multiple Organ Dysfunction Syndrome

Abstract Background Vancomycin (VAN) is the most frequently used antibiotic for hospitalized patients in the United States. Wide use occurs as it is a first line treatment for severe gram-positive bacterial infections in both adults and pediatrics. Most VAN dosing in children with multiple organ dysfunction syndrome (MODS) is guided via population pharmacokinetic (PK) approaches based on creatinine clearance equations and frequent samplings. Volumetric absorptive microsampling (VAMS) offers sampling procedures that require as little as 20 mcL of blood. The primary objective of this study was to develop a population PK (popPK) model for VAN in children with MODS using VAMS.Table 1.Population parameter estimation values. Methods We conducted a multi-center prospective observational study of VAN PK in pediatric MODS. Subjects (< 18 years) participating in a larger observational study of children with MODS in the ICU were eligible. Up to 15 PK samples were collected over 3 consecutive days, along with pertinent covariate data. Parametric popPK modeling was performed using Monolix 2024R1. Covariate selection was based on improvements in objective function value and physiologic relevance. Children on CRRT were excluded from the present analyses. Subjects in our interim analysis were also excluded from analysis if they were missing demographic information or were pending data queries on VAN concentrations.Figure 1.Individual weighted residuals according to time and individual predictions. Results 52 subjects from 8 sites were included with a median age of 6.5 y (range: 2 m–17 y) and a median weight of 24.5 kg (range: 3-214). A two-compartment model with clearance adjusted for allometric scaling and estimated glomerular function best described the data. Population predictions were modest (R2=0.28) whereas individualized predictions were improved (R2=0.61). Between subject variation for clearance and volume of distribution was high even after correcting for U25 creatinine clearance (92.7% and 107.9%, CV% respectively).Figure 2.Observed vs. population (A) and individual (B) predictions for the model. Conclusion Children with MODS display high between subject variability of VAN clearance and volume of distribution. Frequent sampling is needed because “stead-state” does not exist in the early period of MODS for children. VAMS can facilitate small volume sampling and future Bayesian models based on data such as these can facilitate fewer sampling events to classify the patient’s dynamic clearance. Disclosures Nathaniel J. Rhodes, PharmD MS, Apothecademy, LLC: Advisor/Consultant Kevin J. Downes, MD, Paratek, Inc.: Grant/Research Support|Veloxis Pharmaceuticals, Inc.: Grant/Research Support Marc H. Scheetz, PharmD, MSc, Abbvie: Advisor/Consultant|Basilea: Advisor/Consultant|Cidara: Advisor/Consultant|DoseMe: Advisor/Consultant|Entasis: Advisor/Consultant|F2G: Advisor/Consultant|GSK: Advisor/Consultant|Lykos: Advisor/Consultant|Roche: Advisor/Consultant|Third Pole Therapeutics: Advisor/Consultant|Xelia: Advisor/Consultant

Optimization of Low-Carbon Operation in a Combined Electrical, Thermal, and Cooling Integrated Energy System with Liquid Carbon Dioxide Energy Storage and Green Certificate and Carbon Trading Mechanisms

The liquid carbon dioxide energy storage system (LCES), as a highly flexible, long-lasting, and environmentally friendly energy storage technology, shows great potential for application in integrated energy systems. However, research on the combined cooling, heating, and power supply using LCES in integrated energy systems is still limited. In this paper, an optimized scheduling scheme for a low-carbon economic integrated energy system is proposed, coupling LCES with power-to-gas (P2G) technology and the green certificate/carbon trading mechanism. Mathematical models and constraints for each system component are developed, and an optimization scheduling model is constructed, focusing on the economic and low-carbon operation of the integrated energy microgrid system. The objective function aims to minimize total system costs. A case study based on a northern China park is conducted, with seven scenarios set for comparative optimization analysis. The results demonstrate that the use of the combined cooling, heating, and power LCES system reduces total costs by USD 2,706.85 and carbon emissions by 34.57% compared to the single-energy flow operation. These findings validate the effectiveness of the proposed model in optimizing system costs and reducing carbon emissions.

GI-based maximum envelope spectrum deconvolution for machinery fault diagnosis in wind turbine generator

Deconvolution methods utilizing time-domain sparsity as the objective function are widely adopted for machinery fault diagnosis due to their capability in adaptive filter design and attenuation of fault transmission. However, this approach presents several limitations, such as sensitivity to noises and reliance on prior knowledge. To address these challenges, this study introduces the Gini index (GI), derived from economics, as a replacement for time-domain sparsity in maximum envelope spectrum deconvolution. The GI is formulated as the objective function to mitigate the influence of impulse noises. Furthermore, the eigenvector algorithm is incorporated to optimize filter parameters in the proposed method. Finally, simulation and experimental results in a wind turbine generator demonstrate that the developed Gini-based maximum envelope spectrum deconvolution method has greater robustness against random impulse noises and non-fault harmonics.

A sustainable and resilient humanitarian relief chain network design for distributing assembled relief items dynamically considering perishability, under disruption

Purpose Due to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under disruption. Design/methodology/approach This study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was applied. Findings The main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people’s pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were analyzed. Practical implications To verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural disasters. Originality/value This paper introduced a two-stage stochastic mathematical model for designing a resilient humanitarian relief chain network under disruption, at pre- and post-disaster stages. Also, economic and social dimensions of sustainability are considered in this study. Moreover, assembling perishable and im-perishable relief items as relief kits, dynamically is a main contribution of this research.

Directed capacity-preserving subgraphs: hardness and exact polynomial algorithms

We introduce and discuss the Minimum Capacity-Preserving Subgraph (MCPS) problem: given a directed graph with edge capacities cap and a retention ratio α∈(0,1), find the smallest subgraph that, for each pair of vertices (u, v), preserves at least a fraction α of a maximum u-v-flow’s value. This problem originates from the practical setting of reducing the power consumption in a computer network: it models turning off as many links as possible, while retaining the ability to transmit at least α times the traffic compared to the original network. First we prove that MCPS is NP-hard already on a restricted set of directed acyclic graphs (DAGs) with unit edge capacities. Our reduction also shows that a closely related problem (which only considers the arguably most complicated core of the problem in the objective function) is NP-hard to approximate within a sublogarithmic factor already on DAGs. In terms of positive results, we present two algorithms that solve MCPS optimally on directed series-parallel graphs (DSPs): a simple linear-time algorithm for the special case of unit edge capacities and a cubic-time dynamic programming algorithm for the general case of non-uniform edge capacities. Further, we introduce the family of laminar series-parallel graphs (LSPs), a generalization of DSPs that also includes cyclic and very dense graphs. Their properties allow us to solve MCPS on LSPs by employing our DSP-algorithms as subroutines. In addition, we give a separate quadratic-time algorithm for MCPS on LSPs with unit edge capacities that also yields straightforward quadratic time algorithms for several related problems such as Minimum Equivalent Digraph and Directed Hamiltonian Cycle on LSPs.

Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation

Borehole trajectory optimization is a key issue in oil and gas drilling engineering. The traditional wellbore trajectory design method faces great challenges in optimizing the trajectory length and complexity, and it is difficult to meet the actual engineering requirements. In this paper, the three-stage wellbore trajectory optimization problem is studied, and a multi-objective optimization model including two objective functions of trajectory length and trajectory complexity is constructed. In this paper, an improved multi-objective particle swarm optimization algorithm is proposed, which combines the clustering strategy to improve the diversity of solutions, and enhances the local search ability and global convergence performance of the algorithm through the elite learning strategy. In order to verify the performance of the algorithm, comparative experiments were carried out using classical multi-objective benchmark functions. The results showed that the improved algorithm is superior to the traditional method in terms of diversity and convergence of solutions. Finally, the proposed algorithm was applied to the actual three-stage wellbore trajectory optimization problem. In summary, the research results of this paper provide theoretical support and engineering practice methods for wellbore trajectory optimization, and serve as an important reference for further improving the efficiency and quality of wellbore trajectory design.

A Robust Algorithm for Solving Heterogeneous Systems of Equations arising in Kinetostatics of Compliant Mechanisms

Abstract Design and analysis of compliant mechanisms draw constraints from both kinematics and statics, resulting in heterogenous systems of equations. This heterogeneity makes the system numerically ill-conditioned and not amenable to entire solution set (conversely seen for rigid mechanisms), thus burdening Jacobian-based solution procedures and the underlying design or analysis. This paper proposes a robust Restrained line search for Newton's method for the numerical solution of such heterogeneous systems. This novel line search i) prioritizes accuracy of the underlying function approximation over the widely used minimization of the functional residual error and ii) employs a dual objective function definition with closed-form Hessian formulation to maximize performance. It addresses gaps existing in the classical Newton's method, and makes the method truly localized, even more than the MATLAB's nonlinear solver ‘fsolve’. Examples from 2D inverse and 14D coupled kinetostatics of compliant mechanisms are presented to demonstrate the robustness of the proposed algorithm, rendering it into a black-box solver for kinetostatics problems.

Optimal Sizing and Techno-economic Analysis of Combined Solar Wind Power System, Fuel Cell and Tidal Turbines Using Meta-heuristic Algorithms: A Case Study of Lavan Island

Combined renewable energy sources (RESs) are emerging as a competitive alternative to conventional energy production facilities due to their sustainability and zero-emission characteristics. However, determining the optimal system size is complicated by two major challenges: the cost of energy (COE) and the intermittent nature of RESs. This study introduces a novel mathematical approach to optimize the sizing of photovoltaic (PV), wind, hydrogen, battery, and fuel cell systems with electrolyzers, specifically tailored for the remote area of Lavan Island. The proposed method aims to deliver electricity without reliance on the traditional electricity distribution grid, while offering a scalable solution applicable to other geographical regions. The primary objective is to achieve cost-effective electricity generation while ensuring a reliable energy supply through the evaluation of system reliability indices. A fuzzy logic system is employed to minimize the costs of a hybrid system incorporating hydroelectric, wind, solar, and battery technologies, while simultaneously calculating two key reliability metrics: the Loss of Power Supply Probability (LPSP) and the Dump Energy Probability (DEP). To optimize the objective function, this study applies three advanced algorithms: the Shuffled Frog Leaping Algorithm (SFLA), the Grasshopper Optimization Algorithm (GOA), and the Honey Badger Algorithm (HBA). These algorithms are used to determine the global optimum, with comparative analyses conducted to highlight the performance of the proposed approach. The results are evaluated based on statistical metrics, including consistency, execution time, convergence speed, and the minimization of the objective function. The findings demonstrate the superiority and the reliability of the proposed method over alternative approaches, paving the way for cost-efficient and sustainable energy solutions in isolated regions.

Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning. This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. In the first stage, the model is trained using classical gradient descent, and relevant features are extracted; in the second stage, an objective function that minimizes the false positive rate is constructed to obtain a network model with high accuracy and low false positive rate. Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. The results show that the model has made significant progress, with the false positive rate reduced to 11.3% and 7.5%, and the area under the ROC curve increased to 92.8% and 97.01%.

Research on Optimized Multi‐Objective FCS‐MPC Without Weighting Factors of NPC Three‐Level Inverter

ABSTRACTTo address a series of issues in the application of finite control set model predictive control (FCS‐MPC) strategy for neutral point clamped (NPC) three‐level inverters, such as large amount of rolling optimization calculation, poor midpoint voltage balance effect, high common mode voltage, and complex design of weighting coefficients, this paper proposes an optimized multi‐objective FCS‐MPC strategy without weighting factors. First, the method applies the idea of space vector region division, to obtain the optimal region based on Lyapunov function, and combines with delay compensation, reducing the system's optimization time and enhancing the control precision of the system. Second, aiming at the issue of multi‐objective control for inverters, by constructing the objective function to control tracking reference current, midpoint voltage balance, and reduce common mode voltage, a hierarchical optimization control method is adopted. This approach eliminates the need to design complex weighting coefficients constrained in the objective function as constraints, thereby improving the accuracy of predicted currents. Finally, the optimized multi‐objective FCS‐MPC control strategy is validated through simulation and experimentation to achieve a better balance of midpoint voltage and reduce common mode voltage and harmonic content, thereby enhancing the control precision and dynamic/static performance of the system.

Parallel Bayesian optimization of free-electron lasers based on laser wakefield accelerators

Abstract In this paper, the parallel Bayesian optimization algorithm is investigated for the simulation optimization of compact free electron lasers (FELs) driven by laser wakefield accelerators (LWFAs). The radiation energy serves as the objective function, which can be optimized to 25 μJ after several tens of iterations, initiating from random input samplings in soft x-ray regimes. The analysis of the FEL gain process has revealed that the localized properties of the electron beam (e beam) can be concurrently optimized, ensuring a high efficiency of energy extraction from the e beam. Our proposed scheme not only presents a viable design for compact FELs driven by LWFAs in the soft x-ray regime utilizing the start-to-end model, but also highlights the great potential of parallel Bayesian optimization algorithms for tackling the challenges associated with costly and complex black-box optimization problems.

Distributed planning method for detection array of UAV formation with bearing-only sensor network

Abstract Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

An Improved Marriage in Honey-Bee Optimization Algorithm for Minimizing Earliness/Tardiness Penalties in Single-Machine Scheduling with a Restrictive Common Due Date

This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. The goal is to find the optimal sequence for completing a set of tasks on a single machine, minimizing the total penalty incurred for tasks being completed too early or too late compared to their deadlines. To achieve this goal, the study adapts the MBO metaheuristic by introducing modifications to optimize the objective function and produce high-quality solutions within reasonable execution times. The novelty of this work lies in the application of MBO to the single-machine weighted earliness/tardiness problem, an approach previously unexplored in this context. MBO was evaluated using the test problem set from Biskup and Feldmann. It achieved an average improvement of 1.03% across 280 problems, surpassing upper bounds in 141 cases (50.35%) and matching or exceeding them in 193 cases (68.93%). In the most constrained problems (h = 0.2 and h = 0.4), the method achieved an average improvement of 3.77%, while for h = 0.6 and h = 0.8, the average error was 1.72%. Compared to other metaheuristics, MBO demonstrated competitiveness, with a maximum error of 1.12%. Overall, MBO exhibited strong competitiveness, delivering significant improvements and high efficiency in the problems studied.

A Globalization of L-BFGS and the Barzilai–Borwein Method for Nonconvex Unconstrained Optimization

We present a modified limited memory BFGS (L-BFGS) method that converges globally and linearly for nonconvex objective functions. Its distinguishing feature is that it turns into L-BFGS if the iterates cluster at a point near which the objective is strongly convex with Lipschitz gradients, thereby inheriting the outstanding effectiveness of the classical method. These strong convergence guarantees are enabled by a novel form of cautious updating, where, among others, it is decided anew in each iteration which of the stored pairs are used for updating and which ones are skipped. In particular, this yields the first modification of cautious updating for which all cluster points are stationary while the spectrum of the L-BFGS operator is not permanently restricted, and this holds without Lipschitz continuity of the gradient. In fact, for Wolfe–Powell line searches we show that continuity of the gradient is sufficient for global convergence, which extends to other descent methods. Since we allow the memory size to be zero in the globalized L-BFGS method, we also obtain a new globalization of the Barzilai–Borwein spectral gradient (BB) method. The convergence analysis is developed in Hilbert space under comparably weak assumptions and covers Armijo and Wolfe–Powell line searches. We illustrate the theoretical findings with numerical experiments. The experiments indicate that if one of the parameters of the cautious updating is chosen sufficiently small, then the modified method agrees entirely with L-BFGS/BB. We also discuss this in the theoretical part. An implementation of the new method is available on arXiv.

Robust optimal model of green vendor-managed inventory for carbon emission reduction and supply chain visibility

Green supply chain management (SCM) and the green vendor-managed inventory (VMI) strategy are increasingly becoming the focus of the academic world. This paper innovatively investigates the green VMI problems in a three-tier supply chain network, especially incorporating multiple constraints such as supply chain visibility (SCV), quality constraints, carbon emission allowance trading mechanism and green technology investment. To solve this multi-constraint problem, a robust multi-objective optimisation model is constructed in this paper to handle the demand uncertainty. Example values verify the model’s performance, and the sensitivity analysis of critical parameters is carried out. The results show that the robust model can effectively realise the optimal solution of the whole multi-objective system by balancing the optimal value of a single objective function under the multi-objective model. In addition, maintaining a low defect rate and delivery delay rate is crucial for efficient supply chain operation. Furthermore, SCV and supply chain green efficiency need to be balanced, and the pursuit of high SCV will inevitably increase the carbon emission cost of the supply chain system. However, the results also show that investment in green technology R&D can help to improve this situation and realise the effective reduction of carbon emissions while increasing SCV.

Reliability study on crack propagation life of aluminum alloy repair structure

In this paper, the life reliability analysis of AL-AL repair structure is studied. Firstly, the fatigue life of the repair structure is predicted by finite element numerical modeling combined with Forman’s empirical formula, and the influence law of the variation of the repair structure parameters on the fatigue life is investigated. Secondly, the uncertain factors affecting the fatigue life of the structure are considered, the objective function of the structure is linearly fitted, and the fatigue reliability model of the repair structure is established, and the reliability of the structure is obtained by calculating the probability of predicting the fatigue life to be greater than the allowable life. Finally, the parameters of the repair plate that affect the life reliability are analyzed and studied. The results show that under the same fatigue life, the order of parameters affecting the reliability of the repair structure is: repair plate position x > repair plate width w > repair plate length l.

Online Kernel-Based Mode Learning

The presence of big data, characterized by exceptionally large sample size, often brings the challenge of outliers and data distributions that exhibit heavy tails. An online learning estimation that incorporates anti-outlier capabilities while not relying on historical data is therefore urgently required to achieve robust and efficient estimators. In this paper, we introduce an innovative online learning approach based on a mode kernel-based objective function, specifically designed to address outliers and heavy-tailed distributions in the context of big data. The developed approach leverages mode regression within an online learning framework that operates on data subsets, which enables the continuous updating of historical data using pertinent information extracted from a new data subset. By amalgamating the asymptotic distribution functions generated by local mode estimators from all data subsets, the newly suggested estimator is efficiently updated by minimizing a weighted least squares-type loss function. To facilitate this process, we suggest a modified mode expectation-maximization algorithm for numerical optimization, ensuring both storage friendliness and computational efficiency. We demonstrate that the resulting estimator is asymptotically equivalent to the mode estimator calculated using the entire dataset, provided that the covariates in each data subset are homogeneous and the errors are homoskedastic. Monte Carlo simulations and an empirical study are presented to illustrate the finite sample performance of the proposed estimator. Supplemental materials for this paper are available online.

Privacy-Preserving Incentive Allocation for Fair and Resilient Data Sharing in Resource-Constrained Edge Computing Networks

Efficient and secure data sharing is paramount for advancing modern digital ecosystems, especially within edge computing environments characterized by resource-constrained nodes and dynamic network topologies. In such settings, privacy preservation, computational efficiency, and system resilience are critical for user engagement and overall system performance. However, existing approaches face three primary challenges: (i) limited optimization of privacy protection and absence of dynamic privacy budget scheduling for resource-constrained scenarios, (ii) static incentive mechanisms that overlook individual differences in data quality and resource consumption, and (iii) inadequate strategies to ensure resilience in environments with limited resources and unstable networks. This paper introduces the Federated Learning-based Dynamic Incentive Allocation Framework (FL-DIAF) to address these issues. FL-DIAF integrates differential privacy into the federated learning paradigm deployed on edge nodes, enabling collaborative model training that safeguards individual data privacy while maintaining computational efficiency and system resilience. Additionally, the framework employs a Shapley value-based dynamic incentive allocation model to ensure equitable and transparent distribution of incentives by accurately quantifying each participant’s contribution within an elastic edge computing infrastructure. Comprehensive experimental evaluations on diverse datasets demonstrate that FL-DIAF achieves a 9.573% reduction in the objective function value under typical conditions and attains a 100% task completion rate across all tested resilient edge scenarios.

Multi-objective optimization of bleed slot mechanism for a radial flow compressor based on stall control criteria

In radial flow compressors, the ability to achieve a delayed surge margin represents a significant aspect of the design process. The challenge in optimizing surge delay techniques arises from the inherent difficulty of measuring instability within the confines of steady-state simulations. In this study, our goal is to define the objective functions necessary for optimizing surge delay techniques, thereby eliminating the direct calculation of surge margin. Using multi-objective optimization with a variety of configurations, we produced three bleed slot designs for a radial flow compressor. Subsequently, the performance of the three optimal designs were evaluated through LES and RANS simulations. To ensure a fair comparison, an unsteadiness indicator was developed that includes both the fluctuation and the average of the mass flow rate. The results indicated that reducing the incidence angle while simultaneously maximizing the pressure ratio and efficiency resulted in a bleed slot that was more effective in delaying impeller stalling. Based on the analysis of the mass flow rate across 26 impeller rotations, the optimal bleed slot demonstrated the most pronounced enhancement in impeller stability, with a 31% increase. The channel length and width of the optimized bleed slot were found to be 81% and 6% of the impeller radius, respectively. The optimized slot resulted in a flow recirculation of approximately one-fifth of the machine’s overall flow rate. This yielded an estimated increase of 2.1% in the pressure ratio of the base compressor under near-surge conditions. However, this came at the expense of a 1.3% reduction in efficiency. It is postulated that inflow excitation is the primary cause of airfoil surge delay, which is governed by the bleed flow rate and the dynamics of the bleed cells. The optimization results also confirmed the existence of a direct correlation between the incidence angle and the rate of entropy generation at the leading edge.

Multi-Channel Power Scheduling Based on Intrusion Detection System Under DDoS Attack: A Starkberg Game Approach

This study aims to explore the optimal power allocation problem under Distributed Denial of Service (DDoS) attack in wireless communication networks. The Starkberg Equilibrium (SE) framework is employed to analyze the strategic interactions between defenders and attacker under conditions of incomplete information. Considering the energy constraints of both sensors and attacker, this paper also proposes an Intrusion Detection System (IDS) based on remote estimation to achieve an optimal defense strategy, with Packet Reception Rate (PPR) serving as a criterion for intrusion detection. Targeting leaders and followers, the optimal power allocation solution is derived with Signal-to-Interference-Noise Ratio (SINR) and transmission cost as the objective functions. By combining the Adaptive Penalty Function (APF) method with the Differential Evolution (DE) algorithm, the study effectively addresses related non-linear and non-convex optimization problems. Finally, the effectiveness of the proposed method is verified through case studies.