Iterative Optimization Research Articles

Dynamic loss yielding more transferable targeted adversarial examples

Adversarial examples are known to have the property of transferability; as a result, deep neural networks can be compromised by transfer-based attacks in black-box scenarios. Recent studies have shown that targeted adversarial examples are far more difficult to transfer than untargeted adversarial examples. We find that the widely used pattern for targeted attacks (i.e., minimizing the loss with respect to the target label) is insufficient for the transfer scenario. To address this issue, we propose a dynamic loss to yield more transferable targeted adversarial examples. In each iteration of attack optimization, in addition to minimizing the loss with respect to the target label, we dynamically select the top k labels (excluding the target label) with high classification probability to penalize (i.e., maximize) their corresponding loss. As a result, the probability of adversarial examples being classified as target labels will be significantly higher than that of being classified as other labels. Extensive experiments on ImageNet and the Google Cloud Vision API show that the dynamic loss significantly outperforms the traditional loss. For example, in the ensemble-based transfer scenario, the dynamic Cross-Entropy (CE) loss can improve the transferability of targeted adversarial examples by 2∼6 times relative to the traditional CE loss. Code is available at https://github.com/mingcheung/Targeted-Transfer-with-Dynamic-Loss.

Static Equivalence Method of Power Grid Based on Genetic Algorithm

When the electromagnetic transient simulation of a large-scale power grid is carried out, because the simulation is limited by the scale of simulation software, it is necessary to divide the grid into internal and external grids, and the external grid is modeled with equivalent simplification. Aiming at resolving the difficulties of the traditional grid equivalence method, such as a cumbersome calculation process and harsh calculation conditions, a static grid equivalence method based on the genetic algorithm is proposed in this paper. The method first constructs an external grid equivalence network, which includes coupling branches between boundary nodes, and each boundary node is connected to the external grid equivalence power supply through the branches. Then, the external grid equivalence model is used to establish an optimization model for solving the equivalence network parameters based on the information of the internal network, the difference of the power input from the external grid to the boundary nodes before and after equivalence is used as the objective function, and the genetic algorithm is used to realize the iterative optimization of the objective function to obtain a set of optimal post-equivalence network model parameters so that the state variables of the internal power grid before and after the equivalence are matched. The accuracy and effectiveness of the proposed method are verified through simulation with the CERPI36V7 node system.

Optimization of energy efficiency for offshore wind farms via wake modeling-free NMPC

This paper presents an energy efficiency improvement scheme for offshore wind farms. This scheme uses learning-based nonlinear model predictive control (NMPC) and does not require wake modeling. The main feature is that each wind turbine can independently complete all optimal control, without the need for wind farm wake model calculation and iterative optimization. Specifically, this paper studies the critical factors for increasing the active power of wind farms and reducing the structural load of wind turbines, and reconstructs the optimal control objectives of wind turbines. Furthermore, combined with the nonlinear model of wind turbines containing uncertainty, a nonlinear model predictive control algorithm based on deep neural networks was designed, which can achieve multi-objective optimal control at the wind turbine level. The simulation results show that the uncertainty estimation method can effectively improve the nonlinear control performance, achieve an increase in the active power of the wind farm without wake iterative optimization, and at the same time suppress the structural loads of all wind turbines.

Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.

Derivative-Free Iterative One-Step Reconstruction for Multispectral CT.

Image reconstruction in multispectral computed tomography (MSCT) requires solving a challenging nonlinear inverse problem, commonly tackled via iterative optimization algorithms. Existing methods necessitate computing the derivative of the forward map and potentially its regularized inverse. In this work, we present a simple yet highly effective algorithm for MSCT image reconstruction, utilizing iterative update mechanisms that leverage the full forward model in the forward step and a derivative-free adjoint problem. Our approach demonstrates both fast convergence and superior performance compared to existing algorithms, making it an interesting candidate for future work. We also discuss further generalizations of our method and its combination with additional regularization and other data discrepancy terms.

High-precision teeth reconstruction based on automatic multimodal fusion with CBCT and IOS

In digital orthodontic treatment, the high-precision reconstruction of complete teeth, encompassing both the crown and the actual root, plays a pivotal role. Current mainstream techniques, prioritizing the high resolution of intraoral scanned models (IOS), are confined to using IOS data for orthodontic treatments. However, the lack of root information in the IOS data may lead to complications such as dehiscence. In contrast, Cone Beam Computed Tomography (CBCT) data encompasses comprehensive dental information with roots. Nonetheless, the radiative character of CBCT scans renders patients unsuitable for repeated examinations in a short time. In addition, lower scanning precision of CBCT leads to suboptimal teeth segmentation outcomes, hindering the accurate representation of dental occlusal relationships. Therefore, in order to fully utilize the complementarity between dental multimodal data, we propose a method for high-precision 3D teeth model reconstruction based on IOS and CBCT, which mainly consists of global rigid registration and local nonrigid registration. Specifically, we extract the priori information of dental arch curves for coarse alignment to provide a good initial position for the Iterative Closest Point (ICP) algorithm, and design a conformal parameterization method for a single tooth to effectively obtain the point correspondence between IOS and CBCT crowns. The rough crown of the CBCT will gradually fit towards the IOS through iterative optimization of nonrigid registration. The experimental results show that our method robustly fuses the advantageous features of IOS and CBCT. The 3D teeth model reconstructed by our method contains the high-precision crown of IOS and the real root of CBCT, which can be effectively used in clinical orthodontic treatment.

Learning label smoothing for text classification.

Training with soft labels instead of hard labels can effectively improve the robustness and generalization of deep learning models. Label smoothing often provides uniformly distributed soft labels during the training process, whereas it does not take the semantic difference of labels into account. This article introduces discrimination-aware label smoothing, an adaptive label smoothing approach that learns appropriate distributions of labels for iterative optimization objectives. In this approach, positive and negative samples are employed to provide experience from both sides, and the performances of regularization and model calibration are improved through an iterative learning method. Experiments on five text classification datasets demonstrate the effectiveness of the proposed method.

An Empirical Study on Cybersecurity Awareness, Cybersecurity Concern, and Vulnerability to Cyber-attacks

The research aimed to delve into the landscape of cybersecurity awareness and concern levels within Punjab, India, shedding light on the specific demographic groups most susceptible to cyber-attacks. Through meticulous survey administration and data analysis, the study pinpointed a noteworthy vulnerability: young adult females aged 18 to 29 residing in rural Punjab, particularly those either engaged in student pursuits or currently unemployed. This demographic emerged as disproportionately prone to cyber threats. Highlighting the gravity of this finding, the research underscores the urgency for tailored interventions to fortify the cyber defenses of this vulnerable population segment. A crucial facet of this approach involves the development of a prototype aimed at augmenting cybersecurity awareness and fostering ethical online conduct among the identified demographic. This prototype seeks to serve as a platform for delivering enhanced training modules tailored to the unique needs and challenges faced by young females in rural Punjab. Moreover, the paper delineates a comprehensive roadmap for the implementation and evaluation of these recommendations. Central to this plan is the deployment of an application based on the proposed prototype, designed to facilitate the delivery of targeted cybersecurity education and gauge its efficacy in bolstering cyber resilience among the identified demographic. This research not only illuminates the cybersecurity landscape in Punjab but also charts a proactive course of action aimed at mitigating vulnerabilities and empowering at-risk populations through informed intervention strategies. Through ongoing data collection and evaluation efforts, the efficacy of these interventions will be rigorously assessed, driving iterative refinement and optimization of cybersecurity initiatives tailored to the unique needs of Punjab's populace.

Solidification process of ABO3-type perovskites: Kinetic two-phase growth method with optimized potential

The functional properties of ABO3-type perovskite materials have garnered extensive attention, yet their solidification properties have remained challenging to investigate due to high-temperature environments and characterization limitations. In this study, molecular dynamics simulations are employed to comprehensively explore the melting, quenching, and crystal growth processes of SrTiO3 (STO) structural evolution. Through iterative fitting and optimization of ion effective charge, a set of potential functions capable of accurately simulating the high-temperature melting of STO are derived. The melting point obtained for STO (2403 K) using the two-phase coexistence method closely corresponds to the empirical value (2333 K), affirming the precision of the optimized potential. Quenching the molten STO yields an amorphous structure characterized primarily by Ti-O six-fold coordination, which increased by 13.09% when reduced to 300 K at a cooling rate of 0.1 K/ps. Notably, the results for different cooling rates revealed that slower cooling rates and lower temperatures yielded more ordered amorphous structures. To circumvent the formation of amorphous states during crystal growth of perovskite materials, we have developed a kinetic two-phase growth (KTPG) method. This approach regulates the cooling rate within a solid-liquid two-phase system, maintaining constant low supercooling at the interface to mimic STO crystal growth kinetics. Cooling at 0.01 K/ps to 1760 K leads to a notable transformation, with the percentage of Ti-O six-fold coordination reaching 90%. This signifies substantial progress in achieving crystal growth through this method, highlighting its efficacy in facilitating crystal formation from the melt phase.

A Novel Algorithm for Solving High-Dimensional Poisson Equations Based on Radial Basis Function Neural Networks

As a widely used equation in electrostatics, the Poisson equation has significant research value in numerical solution. The basic principle of existing methods is to divide the solution domain into various grids and solve the numerical solutions at each grid node. Therefore, the accuracy of the solution is strongly correlated with the grid density divided. Based on this, this paper proposes a grid-free numerical calculation method that requires far fewer model parameters than traditional methods, and can ignore the order of the equation to solve high-dimensional Poisson equations. Given a Poisson equation, which has a certain type of boundary condition. A certain number of coordinate points are selected on the solution space and its boundary to construct a dataset. Using automatic differentiation technique to fit the differential operator in the equation, a loss function is constructed by incorporating the given boundary conditions or initial conditions, and the final numerical solution is obtained through iterative optimization algorithms. In the numerical experiment section, the algorithm proposed in this paper was used to solve the two-dimensional and three-dimensional Poisson equations with given exact solutions. The relative errors between the numerical solution and the true solution were [Formula: see text] and [Formula: see text], which are within the acceptable range. This proves that the proposed algorithm is feasible for solving the two-dimensional and three-dimensional Poisson equations with precise solutions. Secondly, the proposed algorithm is used to solve the four-dimensional Poisson equation with first-type boundary conditions, and the relative error range of the solution was within [0,0.56], which successfully extends the algorithm to solve high-dimensional Poisson equations and verifies its feasibility and efficiency in solving high-dimensional Poisson equations regardless of the dimension restriction.

Improved FMM for well locations optimization in in-situ leaching areas of sandstone uranium mines

Rapidly obtaining the coverage characteristics of leaching solution in In-situ Leaching Area of Sandstone Uranium Mines is a necessary condition for optimizing well locations reasonably. In the presented study, the improved algorithm of the Fast Marching Method (FMM) was studied for rapidly solving coverage characteristics to replace the groundwater numerical simulator. First, the effectiveness of the FMM was verified by simulating diffusion characteristics of the leaching solution in In-situ Leaching Area. Second, based on the radial flow pressure equation and the interaction mechanism of the front diffusion of production and injection well flow field, an improved FMM which is suitable for In-situ Leaching Mining, was developed to achieve the co-simulation of production and injection well. Finally, the improved algorithm was applied to engineering practice to guide the design and production. The results show that the improved algorithm can efficiently solve the coverage characteristics of leaching solution, which is consistent with those obtained from traditional numerical simulators. In engineering practice, the improved FMM can be used to rapidly analyze the leaching process, delineate Leaching Blind Spots, and evaluate the rationality of well pattern layout. Furthermore, it can help to achieve iterative optimization and rapid decision-making of production and injection well locations under large-scale mining area models.

Iterative image reconstruction algorithm analysis for optical CT radiochromic gel dosimetry

Background. Modern radiation therapy technologies aim to enhance radiation dose precision to the tumor and utilize hypofractionated treatment regimens. Verifying the dose distributions associated with these advanced radiation therapy treatments remains an active research area due to the complexity of delivery systems and the lack of suitable three-dimensional dosimetry tools. Gel dosimeters are a potential tool for measuring these complex dose distributions. A prototype tabletop solid-tank fan-beam optical CT scanner for readout of gel dosimeters was recently developed. This scanner does not have a straight raypath from source to detector, thus images cannot be reconstructed using filtered backprojection (FBP) and iterative techniques are required. Purpose. To compare a subset of the top performing algorithms in terms of image quality and quantitatively determine the optimal algorithm while accounting for refraction within the optical CT system. The following algorithms were compared: Landweber, superiorized Landweber with the fast gradient projection perturbation routine (S-LAND-FGP), the fast iterative shrinkage/thresholding algorithm with total variation penalty term (FISTA-TV), a monotone version of FISTA-TV (MFISTA-TV), superiorized conjugate gradient with the nonascending perturbation routine (S-CG-NA), superiorized conjugate gradient with the fast gradient projection perturbation routine (S-CG-FGP), superiorized conjugate gradient with with two iterations of CG performed on the current iterate and the nonascending perturbation routine (S-CG-2-NA). Methods. A ray tracing simulator was developed to track the path of light rays as they traverse the different mediums of the optical CT scanner. Two clinical phantoms and several synthetic phantoms were produced and used to evaluate the reconstruction techniques under known conditions. Reconstructed images were analyzed in terms of spatial resolution, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal non-uniformity (SNU), mean relative difference (MRD) and reconstruction time. We developed an image quality based method to find the optimal stopping iteration window for each algorithm. Imaging data from the prototype optical CT scanner was reconstructed and analysed to determine the optimal algorithm for this application. Results. The optimal algorithms found through the quantitative scoring metric were FISTA-TV and S-CG-2-NA. MFISTA-TV was found to behave almost identically to FISTA-TV however MFISTA-TV was unable to resolve some of the synthetic phantoms. S-CG-NA showed extreme fluctuations in the SNR and CNR values. S-CG-FGP had large fluctuations in the SNR and CNR values and the algorithm has less noise reduction than FISTA-TV and worse spatial resolution than S-CG-2-NA. S-LAND-FGP had many of the same characteristics as FISTA-TV; high noise reduction and stability from over iterating. However, S-LAND-FGP has worse SNR, CNR and SNU values as well as longer reconstruction time. S-CG-2-NA has superior spatial resolution to all algorithms while still maintaining good noise reduction and is uniquely stable from over iterating. Conclusions. Both optimal algorithms (FISTA-TV and S-CG-2-NA) are stable from over iterating and have excellent edge detection with ESF MTF 50% values of 1.266 mm−1 and 0.992 mm−1. FISTA-TV had the greatest noise reduction with SNR, CNR and SNU values of 424, 434 and 0.91 × 10−4, respectively. However, low spatial resolution makes FISTA-TV only viable for large field dosimetry. S-CG-2-NA has better spatial resolution than FISTA-TV with PSF and LSF MTF 50% values of 1.581 mm−1 and 0.738 mm−1, but less noise reduction. S-CG-2-NA still maintains good SNR, CNR, and SNU values of 168, 158 and 1.13 × 10−4, respectively. Thus, S-CG-2-NA is a well rounded reconstruction algorithm that would be the preferable choice for small field dosimetry.

Application Research of Intelligent Search System Based on Clustering Algorithm

In this paper, a multi-agent oriented personalized information retrieval method is proposed. The system is composed of 5 agents and 2 other components. The design highlights the personalization and real-time of the information obtained by users, and has the characteristics of intelligent retrieval and active retrieval. Then this paper studies it from three aspects: user model, system learning model and individual information model. Then a grouping gravity search algorithm is proposed. Compared with conventional gravity search, group gravity search is carried out for a specific decoding strategy. This algorithm is a new gravity search algorithm suitable for packet coding, which makes the packet search process approximate to the iterative optimization of classical gravity algorithm. Finally, the effectiveness of the proposed method is verified on various typical experimental cases. Experiments show that the proposed algorithm has higher classification performance than other intelligent group algorithms.

Semi-supervised multi-label feature selection based on dual dynamic graph

Traditional multi-label feature selection is usually performed under the condition of given label information, but nowadays, labeling multi-label data is a huge project, which is both time-consuming and labor-intensive, but if there is no label information condition, it will lead to poor feature selection, in order to solve this problem, this paper proposes a new semi-supervised multi-label feature selection method, i.e., semi-supervised multi-label feature selection algorithm based on dual dynamic graph. In this paper, a semi-supervised multi-label feature selection algorithm is proposed by constructing a dual dynamic graph. First, the method selects the most discriminative features for dimensionality reduction through the feature selection method of least squares regression, combined with the redundancy penalty of highly correlated features. Second, the label information is added to the construction of sample matrix similarity to learn the similarity. A semi-supervised multi-label feature selection framework is constructed by designing iterative updates of dual dynamic graphs to learn more accurate pseudo-label matrices to guide feature selection. Finally, the paper validates the above model using the alternating iteration optimization algorithm and verifies the effectiveness of the algorithm through experiments.

Analytical solution to partial least squares

Partial least squares (PLS) is a widely used multivariate statistical technique, which can be used in neuroimaging, process monitoring, economics, etc. Because the standard PLS is trained by nonlinear iterative partial least squares (NIPALS) algorithm, which can only obtain the numerical solution rather than the analytical solution. Therefore, it is hard for the subsequent theoretical analysis in PLS and the iterative optimization process is computationally intensive. This paper proposes an analytical solution to take the place of NIPALS. This determination meaningfully contributes to future theoretical analysis of PLS. In addition, the analytical solution demonstrates that the selection of initial vector in NIPALS does not affect the PLS training results. Moreover, the analytical solution avoids the multiple iterative calculations, so the divergence of the calculation process will be avoided and the computation burden will be reduced significantly.

Interior point algorithm for second-order cone optimization based on a new kernel function

Second-order cone optimization (SOCO) problems are crucial in various fields such as engineering, finance, and machine learning due to their ability to model complex convex optimization tasks efficiently. In this article, we propose an interior point algorithm tailored for SOCO, leveraging a novel kernel function to enhance optimization performance. Traditional interior point methods for SOCO often encounter challenges in scalability and efficiency, particularly with large-scale problems. These methods typically rely on barrier functions that may suffer from slow convergence rates, especially when dealing with highly ill-conditioned or degenerate cones. To address these limitations, our approach introduces a new kernel function that exploits the geometric properties of second-order cones, thereby improving convergence behavior and computational efficiency. The key innovation of our algorithm lies in the construction of the kernel function, which incorporates insights from the structure of second-order cones to efficiently capture the curvature information of the optimization problem. By exploiting this curvature information, our algorithm can effectively navigate the optimization landscape, leading to faster convergence and enhanced robustness compared to traditional methods. Furthermore, our algorithm is equipped with advanced techniques for handling various challenges encountered in SOCO, such as dealing with degenerate cones and exploiting problem structure to accelerate convergence. Additionally, we incorporate strategies for warm-starting and adaptive step-size selection to further improve efficiency, particularly in iterative optimization processes. Finally, our proposed interior point algorithm for second-order cone optimization, based on a new kernel function, derives the iteration bounds O (Nlog (N/∊)) for large update methods. Here denotes N the number of second-order cones in the problem formulation and ∊ the desired accuracy. These iteration bounds are currently the best known bounds for such methods.

Simulation of a spin-boson model by iterative optimization of a parametrized quantum circuit

Time evolution of the populations of spin states coupled with bosons, which can be a model of photosynthetic excitation energy transfer of dye molecules surrounded by proteins, is simulated using the projected-variational quantum dynamics algorithm. By a transformation of the Hamiltonian describing the spin-boson model into the one-dimensional nearest-neighbor form, it is shown that the spin-boson model can be simulated using the sequential ansatz even if a quantum computer has limited connectivity. The optimization of the parametrized quantum circuits is performed by the gradient descent method on a classical computer using the automatic differentiation, and the population of the spins is simulated on a noisy intermediate-scale quantum computer. The error originating from the quantum computing is mitigated by the Clifford data regression, in which the noise channel is estimated using the data obtained from all the time steps.

Approach for contact medical device development via integrated testing, skeletal muscle modeling, and finite element analysis

Development of novel medical devices for the treatment of musculoskeletal pain associated with neuro-muscular trigger points requires a model for relating the mechanical responses of in vivo biological tissues to applied palliative physical pressures and a method to design treatments for optimal effects. It is reasonable to hypothesize that the efficacy of therapeutic treatment is proportional to the maximum tensile strain at trigger point locations. This work presents modeling of the mechanical behavior of biological tissue structures and treatment simulations, supported by indentation experiments and finite element (FE) modeling. The steady-state indentation responses of the tissue structure of the posterior neck were measured with a testing device, and an FE model was constructed using a first-order Ogden hyperelastic material model and calibrated with the experimental data. The error between experimental and FE-generated displacement-load curves was minimized via a two-stage optimization process comprised of an Optimal Latin Hypercube design-of-experiments analysis and a Bayesian optimization loop. The optimized Ogden model had an initial shear modulus (μ) of 5.16 kPa and a deviatoric exponent (α) of 11.90. Another FE model was developed to simulate the deformation of the tissue structures in the posterior neck adjacent to the C3 vertebrae in response to indentation loading, in order to determine the optimal location and angle to apply an indentation force for maximum therapeutic benefit. The optimal location of indentation was determined to be 28° lateral from the sagittal plane along the surface of the skin, measured from the centerline of the spine, at an angle of 8° counterclockwise from the surface normal vector. The optimized spatial orientation of the indentation corresponded to the average of the maximum principal strain across the deep muscle region of the model.

A New X-ray Diffraction Spectrum-Based Untargeted Strategy for Accurately Identifying Ancient Painted Pottery from Various Dynasties and Locations in China

X-ray diffraction (XRD) is extensively used in archaeometric investigation. Herein, we provide a novel XRD spectrum-based untargeted strategy for the classification of ancient painted pottery for various dynasties. It was accomplished using the original spectrum without a phase identification. To eliminate the influence of baseline drift, a new baseline drift correction algorithm was specifically designed for XRD spectra. The algorithm was implemented using local minimum values in the analyzed signal in an iterative optimization manner. The results indicated that with the aid of the algorithm, the baseline drift problem can be successfully resolved, and the classification of ancient painted pottery can be greatly improved. Finally, the developed strategy was successfully used to discriminate ancient painted pottery from the Han and Tang dynasties in the cities of Guyuan and Zhongwei, China. The developed untargeted strategy had the remarkable advantage of almost automatic data analysis. The toolbox of our strategy can be obtained from the authors.

EWFAIGF: Design of an Efficient Model for Enhancing Energy Efficiency in IoT-Based Wireless Sensor Networks through Fuzzy AHP and Iterative Grey Wolf Jelly Fish Optimization

In the realm of Wireless Sensor Networks (WSNs), the quest for enhanced energy efficiency remains paramount, given their pivotal role in facilitating the Internet of Things (IoT) applications. Existing methodologies often grapple with the dual challenge of optimizing energy consumption while ensuring robust data transmission, thereby limiting their efficacy in dynamic, resource-constrained environments. This work introduces a novel paradigm that meticulously addresses these constraints, thereby heralding a significant leap towards optimizing WSNs' operational efficiency. At the core of our proposed model lies the integration of Fuzzy Analytic Hierarchy Process (Fuzzy AHP)-based clustering, which ingeniously segregates nodes by leveraging multi-criteria such as location, energy efficiency, and temporal performance. This clustering serves as a precursor to the application of an innovative Iterative Grey Wolf Jelly Fish Optimizer (GWJFO). The GWJFO stands out by its strategic prowess in delineating optimal routing paths, thus minimizing energy expenditure and enhancing data relay efficiency. Furthermore, our model is fortified with a Q Learning Method, ingeniously designed to identify and execute optimal alternate routing paths through a Make Before Break strategy. This addition not only mitigates potential faults but also significantly boosts computational efficiency and packet delivery performance. Empirical validation through real-time network simulations underscored the model's superiority, demonstrating a 9.5% improvement in communication speed, an 8.5% increase in energy efficiency, a 4.5% improvement in packet delivery performance under fault conditions, a 10.4% rise in throughput, and a 5.9% enhancement in network consistency over existing benchmarks. This groundbreaking work not only paves the way for more energy-efficient WSNs but also sets a new standard in achieving high-performance metrics essential for the next generation of IoT applications. The implications of these advancements extend beyond mere technical enhancements, offering a beacon for future research in the domain, potentially revolutionizing the way we deploy and manage sensor networks in an array of applications.