Global Search Research Articles

Estimation method of the Homodyned K-distribution based on the Improved Shuffled Frog Leaping Algorithm for ultrasound tissue characterization

Abstract Aiming at promoting the accuracy of the integral value of the probability distribution function (PDF) of Homodyned K-distribution (HK), a new maximum likelihood estimation method for HK distribution parameters is proposed by using the improved shuffled frog leaping algorithm (ADM-SFLA) and Newton-Raphson iterative algorithm (NR). To improve the accuracy of the global search of the traditional SFLA, two variation factors of Gauss and Cauchy are first used to find the global optimal solution in a convergence state. Secondly, to obtain the integral node with optimal segmentation, the ADM-SFLA is used to perform the non-equidistant adaptive segmentation of the integral interval. Then, the PDF of the HK distribution is calculated between each numerical integration cell based on Simpson integral formula. Subsequently, the integral value of the PDF for HK distribution is obtained by summing the integration values from each numerical integration cell. Finally, the optimal estimation of HK distribution parameters is obtained based on NR algorithm. The performance of the proposed method is evaluated and compared through simulated data as well as the envelope signals obtained from the ultrasonic simulation model with different scattering components. The results show that the ADM-SFLA based method can improve the estimation accuracy of HK distribution parameters effectively.

Large language models for automatic equation discovery of nonlinear dynamics

Equation discovery aims to directly extract physical laws from data and has emerged as a pivotal research domain in nonlinear systems. Previous methods based on symbolic mathematics have achieved substantial advancements, but often require handcrafted representation rules and complex optimization algorithms. In this paper, we introduce a novel framework that utilizes natural language-based prompts to guide large language models (LLMs) in automatically extracting governing equations from data. Specifically, we first utilize the generation capability of LLMs to generate diverse candidate equations in string form and then evaluate the generated equations based on observations. The best equations are preserved and further refined iteratively using the reasoning capacity of LLMs. We propose two alternately iterated strategies to collaboratively optimize the generated equations. The first strategy uses LLMs as a black-box optimizer to achieve equation self-improvement based on historical samples and their performance. The second strategy instructs LLMs to perform evolutionary operations for a global search. Experiments are conducted on various nonlinear systems described by partial differential equations, including the Burgers equation, the Chafee–Infante equation, and the Navier–Stokes equation. The results demonstrate that our framework can discover correct equations that reveal the underlying physical laws. Further comparisons with state-of-the-art models on extensive ordinary differential equations showcase that the equations discovered by our framework possess physical meaning and better generalization capability on unseen data.

Development of a method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm

The problems of processing heterogeneous data are discontinuous, undifferentiated, and multimodal. The most common approaches to processing heterogeneous data are swarm intelligence algorithms (swarm algorithms). Given the above, classical gradient deterministic algorithms are inappropriate for solving the problems of processing heterogeneous data. The problem solved in the study is to increase the efficiency of processing heterogeneous data circulating in information systems, regardless of the number of data sources. The object of the study is hierarchical systems. A method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm is proposed. The study is based on the reptile algorithm (RA) for processing heterogeneous data circulating in the system. For RA training, evolving artificial neural networks are used. The originality of the proposed method lies in setting RA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining RA feeding locations, which allows prioritizing the search in a given direction. The next element in the originality of the study is the possibility of choosing an RA hunting strategy, which allows a rational use of available system computing resources. Another original element of the study is determining the initial velocity of each RA. This makes it possible to optimize the speed of exploration of each RA in a certain direction. The method provides a 15–19 % increase in data processing efficiency by using additional improved procedures. The proposed method should be used in processing large amounts of data

Development of a methodical approach to assessing the state of hierarchical systems using a meta-heuristic approach

Optimization is a complex process of defining a set of solutions for a wide range of problems, including management decision-making. One of the approaches to increasing the efficiency of solving optimization problems is metaheuristic algorithms. The problem solved in the study is to increase decision-making efficiency in the problems of assessing the state of hierarchical systems while ensuring a given reliability, regardless of its hierarchy. The object of the study is hierarchical systems. The subject of the study is the decision-making process in management problems using an advanced Tasmanian devil algorithm (TDA) and evolving artificial neural networks. A methodical approach using a metaheuristic algorithm is proposed. For TDA training, evolving artificial neural networks are used. The originality of the proposed method lies in setting TDA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining TDA feeding locations, which allows prioritizing the search in a given direction. The next original element of the study is the possibility of choosing a TDA hunting strategy, which allows a rational use of available system computing resources. Another original element of the study is determining the initial velocity of each TDA. This makes it possible to optimize the speed of exploration by each TDA in a specific direction. Using the methodical approach provides a 14–17 % increase in data processing efficiency by using additional improved procedures. The proposed methodical approach should be used to solve the problems of evaluating hierarchical systems

FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion.

Adaptive spiral flight and multi-strategy fusion are the foundations of a new FOX optimization algorithm that aims to address the drawbacks of the original method, including weak starting individual ergodicity, low diversity, and an easy way to slip into local optimum. In order to enhance the population, inertial weight is added along with Levy flight and variable spiral strategy once the population is initialized using a tent chaotic map. To begin the process of implementing the method, the fox population position is initialized using the created Tent chaotic map in order to provide more ergodic and varied individual beginning locations. To improve the quality of the solution, the inertial weight is added in the second place. The fox random walk mode is then updated using a variable spiral position updating approach. Subsequently, the algorithm's global and local searches are balanced, and the Levy flying method and greedy approach are incorporated to update the fox location. The enhanced FOX optimization technique is then thoroughly contrasted with various swarm intelligence algorithms using engineering application optimization issues and the CEC2017 benchmark test functions. According to the simulation findings, there have been notable advancements in the convergence speed, accuracy, and stability, as well as the jumping out of the local optimum, of the upgraded FOX optimization algorithm.

Accelerating Global Search of Large-Sized Silver Clusters Using Cluster Graph Attention Network.

Great efforts have been devoted to understanding the stability and reactivity of silver clusters, which usually depend on geometric structures, electronic configuration, and cluster size. Despite the fact that the jellium model and Wulff construction rule have successfully rationalized the stable clusters with "magic number" behavior, some experiments imply that silver clusters with 48 valence electrons also possess puzzling enhanced stability. In this work, using a recently developed deep learning technology, i.e., cluster graph attention network (CGANet), combined with a homemade comprehensive genetic algorithm (CGA) program, we searched the global minimum (GM) structures of Agn (n = 30-60) clusters with graphics processing unit acceleration, whose efficiency is about 2 orders of magnitude higher than that of the conventional density functional theory (DFT) calculations. GM structures and some representative isomers are reported at each size, revealing the competitive structural patterns based on truncated octahedra and icosahedra as well as the icosahedra-based layer-by-layer growth mode of large-sized Ag clusters. Most importantly, the size-dependent evolution behavior of structural and electronic properties of Agn (n = 30-60) clusters can successfully explain the observed stability at Ag48. Therefore, CGANet provides a powerful tool for rapidly exploring the potential energy surface of atoms with an accuracy comparable to that of DFT.

Research on pile bearing capacity prediction improved by grey wolf optimization in the SSA-LSSVM model

Abstract Pile foundations in soft soil often encounter challenges regarding abnormal bearing capacity, significantly impacting the safety of engineering projects. The bearing capacity of pile foundations is influenced by various factors, often characterized by complexity and unpredictability. Therefore, this study proposes a comprehensive model, termed GWOASSA-LSSVM, based on Grey Wolf Optimization (GWO)-enhanced Sparrow Search Algorithm (SSA) and Least Squares Support Vector Model (LSSVM). By introducing a grey wolf hierarchy to enhance the global search capability of the sparrow search algorithm and automating the optimization of parameters (γ, δ) in the LSSVM model using GWO, the GWOASSA-LSSVM predictive model is established. Evaluation metrics including correlation, and correlation between pile bearing capacity and the predictive target are considered, with pile bearing capacity as the predictive target. The GWOASSA-LSSVM model is compared with SSA-LSSVM, LSSVM, and Back Propagation Neural Network (BPNN). Results indicate that the GWOASSA-LSSVM model outperforms SSA-LSSVM, LSSVM, and BPNN across metrics such as Coefficient of Determination (R2), Variance Accounted For (VAF), Performance Index (PI), Index of Agreement (IOA), Index of Scatter (IOS), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and a20-index, demonstrating its capability for accurate prediction of pile bearing capacity.

Wave spectrum fitting with multiple parameters based on optimization algorithms and its application

In the realm of ocean engineering and environmental research, the accuracy of the wave spectrum is paramount. Traditional models, constrained by the number of parameters, often struggle to accurately capture the intricacies of real-world wave spectra, especially when confronting the complexities of multi-peak spectral shapes shaped by wind-sea and swell systems. This research develops a novel multi-parameter wave spectrum model that substantially amplifies the capacity to describe the diversity of wave spectra by introducing more shape parameters. The proposed model includes more shape parameters than its predecessors. These parameters can be directly optimized and obtained through the Heterogeneous Comprehensive Learning Particle Swarm Optimizer (HCLPSO). This sophisticated algorithm leverages its robust global search capabilities and swift convergence to fine-tune the model’s parameters, ensuring a high degree of precision in fitting the measured wave spectra. Furthermore, the proposed model also shows great potential in wave spectrum prediction, even when there are fewer training samples, it can still yield relatively accurate predictive results. This research not only improves the applicability and accuracy of the wave spectrum model but also offers a new tool for ocean wave research and real-world applications, thereby contributing important research and practical value to the field.

Prediction model of coal seam gas content based on kernel principal component analysis and IDBO-DHKELM

Abstract Accurate coal seam gas content assists in the effective prevention of coal and gas outburst accidents. To solve this problem, an IDBO-DHKELM coal seam gas content prediction model is proposed by combining improved Dung Beetle optimization algorithm (IDBO) with a deep hybrid kernel extreme learning machine (DHKELM). First, the index factors of the coupled gas content are determined according to the influence factors of coal seam gas content and the actual situation of mine production. The correlation of index factors is analyzed by SPSS 27 software via Pearson correlation coefficient matrix. Then, the principal components of the original data are extracted using the principal component analysis method (KPCA). Second, sine chaotic mapping, fusion improved sinusoidal algorithm, and fusion adaptive Gauss–Cauchy hybrid mutation perturbation are introduced to improve the Dung Beetle optimization algorithm (DBO) to enhance its global search capability. Third, IDBO is used to optimize the number of hidden layer nodes, regularization coefficient, penalty coefficient, and kernel parameter in DHKELM, which improves the prediction accuracy and further avoid the phenomenon of overfitting. Finally, the principal component extracted by KPCA is taken as the model’s input, and the gas content as the model’s output. The results are compared and analyzed with those of PSO-BPNN, GA-BPNN, PSO-SVM, and DPO-DHKELM models. The results demonstrate that the IDBO-DHKELM model’s performance is the best in each performance index. Compared with other models, the mean absolute error of test samples in the IDBO-DHKELM model is reduced by 0.402, 0.4407, 0.3554, and 0.0646, respectively. The mean absolute percentage error is decreased by 3.67%, 4.07%, 8.27%, and 6.35%, respectively. The root mean square error decreased by 0.7861, 0.7148, 0.3384, and 0.1186, respectively. The coefficient of determination (R 2) is increased by 0.1544, 0.1404, 0.0955, and 0.0396, respectively. Finally, the IDBO-DHKELM model and other models are applied to an experimental mine. The resulting IDBO-DHKELM model is the closest to the actual value, which further verifies the universality and reliability of the model. Therefore, the model is more suitable for the prediction of coal seam gas content.

Dynamic nonparametric modeling of sail-assisted ship maneuvering motion based on GWO-KELM

Accurately predicting the state of ship motion is crucial for ship navigation control with energy efficiency optimization studies. When modeling the motion of novel ships equipped with propulsion devices such as sails, it is essential to consider the effects of sail thrust and lateral forces on the ship's motion states. Building upon the analysis of sail force and sail-assisted ship motion modeling, this paper proposes a ship motion dynamic modeling method utilizing Kernel Extreme Learning Machine (KELM) regression. By optimizing the hyperparameters of the kernel function using the Grey Wolf Optimizer (GWO) algorithm and incorporating a sliding time window for dynamic model updating, the proposed approach combines the efficient learning mechanism of KELM with the global search capability of GWO, effectively enhancing prediction accuracy and timeliness. Utilizing KVLCC2 ship model motion data, simulation data from the sail-assisted ship “New Aden” and real ship data as case studies, comparative analysis with traditional models such as SVM and ELM. The results demonstrate that the GWO-KELM modeling approach exhibits robustness and high prediction accuracy, the MAE of the proposed model in the real ship motion prediction effect is reduced by 8.89% and the RMSE is reduced by 9.44% compared with the other models, which significantly enhance the predictive performance of sail-assisted ship maneuvering motion states.

Reference 1D Seismic Velocity Models for Volcano Monitoring and Imaging: Methods, Models, and Applications

Abstract Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real-time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P- and S-wave arrival-time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey-published 1D P-wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.

Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer

Extreme learning machines (ELMs), single hidden-layer feedforward neural networks, are renowned for their speed and efficiency in classification and regression tasks. However, their generalization ability is often undermined by the random generation of hidden layer weights and biases. To address this issue, this paper introduces a Hierarchical Learning-based Chaotic Crayfish Optimization Algorithm (HLCCOA) aimed at enhancing the generalization ability of ELMs. Initially, to resolve the problems of slow search speed and premature convergence typical of traditional crayfish optimization algorithms (COAs), the HLCCOA utilizes chaotic sequences for population position initialization. The ergodicity of chaos is leveraged to boost population diversity, laying the groundwork for effective global search efforts. Additionally, a hierarchical learning mechanism encourages under-performing individuals to engage in extensive cross-layer learning for enhanced global exploration, while top performers directly learn from elite individuals at the highest layer to improve their local exploitation abilities. Rigorous testing with CEC2019 and CEC2022 suites shows the HLCCOA’s superiority over both the original COA and nine renowned heuristic algorithms. Ultimately, the HLCCOA-optimized extreme learning machine model, the HLCCOA-ELM, exhibits superior performance over reported benchmark models in terms of accuracy, sensitivity, and specificity for UCI breast cancer diagnosis, underscoring the HLCCOA’s practicality and robustness, as well as the HLCCOA-ELM’s commendable generalization performance.

Multi-UAV Path Planning Based on Cooperative Co-Evolutionary Algorithms with Adaptive Decision Variable Selection

When dealing with UAV path planning problems, evolutionary algorithms demonstrate strong flexibility and global search capabilities. However, as the number of UAVs increases, the scale of the path planning problem grows exponentially, leading to a significant rise in computational complexity. The Cooperative Co-Evolutionary Algorithm (CCEA) effectively addresses this issue through its divide-and-conquer strategy. Nonetheless, the CCEA needs to find a balance between computational efficiency and algorithmic performance while also resolving convergence difficulties arising from the increased number of decision variables. Moreover, the complex interrelationships between the decision variables of each UAV add to the challenge of selecting appropriate decision variables. To tackle this problem, we propose a novel collaborative algorithm called CCEA-ADVS. This algorithm reduces the difficulty of the problem by decomposing high-dimensional variables into sub-variables for collaborative optimization. To improve the efficiency of decision variable selection in the collaborative algorithm and to accelerate the convergence speed, an adaptive decision variable selection strategy is introduced. This strategy selects decision variables according to the order of solving single-UAV constraints and multi-UAV constraints, reducing the cost of the optimization objective. Furthermore, to improve computational efficiency, a two-stage evolutionary optimization process from coarse to fine is adopted.Specifically, the Adaptive Differential Evolution with Optional External Archive algorithm (JADE) is first used to optimize the waypoints of the UAVs to generate a basic path, and then, the Dubins algorithm is combined to optimize the trajectory, yielding the final flight path. The experimental results show that in four different scenarios involving 40 UAVs, the CCEA-ADVS algorithm significantly outperforms the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and JADE algorithms in terms of path performance, running time, computational efficiency, and convergence speed. In addition, in large-scale experiments involving 500 UAVs, the algorithm also demonstrates good adaptability, stability, and scalability.

Delivering holistic energy-saving operation for heterogeneous all-parallel chiller plant systems through two-stage optimization assisted by input convex neural networks

With the rapid growth of building cooling demand and energy consumption, the potential for energy-saving operations gave rise to the popularity of implementing heterogeneous all-parallel chiller plant systems in large-scale public buildings. Meanwhile, heterogeneity, multi-dimensionality, and tight coupling interactions considerably complicate the holistically optimal operation of the system. Although artificial neural networks (ANN) have achieved great success in complex system identification, the endogenous non-convexity of classical feedforward neural networks (FNN), which are widely applied in model-based optimization for chiller plant operation, makes conventional optimizers prone to falling into local optima, forcing previous methodologies to sacrifice model precision for computational tractability. In this study, a special type of ANN named input convex neural network (ICNN) was leveraged to identify five individual convex models for energy-consuming units in the chiller plant system, providing convex input-output mappings while preserving high-resolution representations. Then, a two-stage optimizer with a sequential integration of a global searcher and a local optimizer was developed to effectively solve the holistic energy-saving problem by optimizing ten decision variables including chilled water supply temperature, partial load ratio of chillers, speed ratios of pumps and cooling tower fans, cooling water flow rate of cooling towers, and on/off states of devices. Finally, the methodology was deployed to a case study using on-site operation data and was compared with conventional candidates. Average holistic energy-saving ratios of 13.37% and 9.14% were achieved with average runtimes of 1.12 s and 1.07 s by the proposed optimizer in the high-load and light-load scenarios, respectively, showcasing more advantageous in resolving the operation optimization of complicated chiller plant systems.

Hybrid differential evolution algorithm for Falkner-Skan flow with rotation

A hybrid differential evolution algorithm is used in this work to study the rotating transport of Falkner-Skan flow. The problem is modeled as an equivalent optimization problem by using Padé rational approximation functions. The primary model of the governing partial differential equations is imposed to subjugate the error between profiles. A hybrid evolutionary algorithm based on differential evolution and a convergent version of Nelder-Mead direct search algorithm is employed to perform global exploratory search along with an enhanced exploitation to improve the accuracy of the proposed solution scheme. The resulting scheme is named as evolutionary Padé approximation (EPA) scheme. The performance of the proposed EPA scheme on the Falkner-Skan boundary value problem is investigated by considering various values of the rotation parameters. The developed optimizer in EPA scheme was able to minimize the residuals up to10−10. Results are displayed graphically in order to study the effect of various types of parameters. EPA scheme determined that angular velocity increases or decreases accordingly as fluid parameter (β) and the rotation parameter (λ) but shows inverse behavior with respect to power law index(n). Similarly, the response of velocity profile along y−axis was decreasing function of β as well as n but increasing function of λ. The performance of the proposed EPA scheme has been demonstrated by comparing results with a hybrid neural network scheme and found in excellent agreement.

Intelligent enhanced particle filter with deep residual network surrogate for accurate groundwater pollution source characterization

Accurate groundwater pollution source characterization (GPSC) is crucial for environmental protection and water resource management. Although the particle filter (PF) algorithm is widely employed for GPSC, it suffers from particle degradation and loss of diversity. To address this challenge, this study proposes an intelligent enhanced particle filter (IEPF) method to enhance GPSC’s accuracy and efficiency. First, this method optimizes the resampling process by defining a weight threshold while introducing an information transmission mechanism. This mechanism enables low-weight particles to update their positions by leveraging information from high-weight particles and neighboring particles, thereby enhancing information exchange among particles and improving the algorithm’s global search capability. Crossover and mutation operations are introduced to further increase the particle diversity, effectively preventing particles from concentrating on local optimal solutions. Furthermore, this study employs a deep residual network (ResNet) as a surrogate model for the simulation model. The ResNet has strong feature learning and expression capabilities and efficiently captures nonlinear relationships in data, thus significantly improving computational efficiency while ensuring accuracy. The results demonstrate that the IEPF method significantly enhances GPSC accuracy. In summary, the proposed IEPF method combined with the ResNet surrogate model provides an efficient and accurate approach to GPSC, with considerable benefits for groundwater pollution management.

MICFOA: A Novel Improved Catch Fish Optimization Algorithm with Multi-Strategy for Solving Global Problems.

Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm's ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO.

A Hindmarsh–Rose neuron model with electromagnetic radiation control for the mechanical optimization design

This paper proposes a Hindmarsh–Rose(HR) neuron model controlled by electromagnetic radiation to solve the mechanical optimization design problem. A novel memristor for simulating electromagnetic radiation is established to control the dynamical behavior of HR neuron models. By analyzing the HR model and using the numerical analysis methods of phase portrait, bifurcation diagram, Lyapunov exponent and spectral entropy (SE) complexity, the HR model is shown to have a hidden attractor and rich chaotic dynamics. Then, a chaotic optimization algorithm based on HR neuron model is designed to solve the mechanical optimization design of tension/compression coil springs and pressure vessels. The chaotic sequence of this algorithm which has high-quality pseudo-random number, has stronger global search ability. Simulation of two mechanical optimization design problems in comparison with some well-known optimization algorithms demonstrates the superiority of the HR in the solution quality.

Global search algorithms in automated design of starting points for deep-UV lithography objective

Global search algorithms in automated design of starting points for deep-UV lithography objective

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.