Optimization Algorithm Research Articles

Behavior modeling for a new flexure-based mechanism by Hunger Game Search and physics-guided artificial neural network

Compliant mechanism has some advantages and has been widely applied in many accurate positioning systems. However, modeling the compliant mechanism behavior has suffered from many challenges, such as unstable results, and the limitation of training data set. In the field of compliant mechanism modeling, there has been no research interested in applying meta-heuristics optimization algorithms to optimize the weights and biases of the neural network globally. Additionally, the Physics-Guided Artificial Neural Network, a research direction that has received much attention recently, has not been considered in problems related to compliant mechanisms. In order to surmount those drawbacks, this paper pioneers a new approach to model behaviors of a compliant mechanism using the Hunger Game Search and the Physics-Guided Artificial Neural Network. The Hunger Game Search can directly search the model’s weights and biases so that the target function that takes advantages of both physical and data information can be minimized. The investigations on diverse training set ratios and the ANOVA tests at the significance level reveal that using the Hunger Game Search can result in smaller errors than using the backpropagation method. Furthermore, applying the Hunger Game Search to a Physics-Guided Artificial Neural Network not only can reduce error but also can increase convergence speed compared to applying Hunger Game Search to conventional neural networks. Those results demonstrate the potential of the proposed method in modeling the behavior of compliant mechanisms.

Combining Differential Privacy with FedAvgM to Optimize Data Access Control in the Information Platform

In response to the privacy leakage risk and low-efficiency issues of traditional data access control, especially in distributed environments, data transmission and centralized processing may expose sensitive information. To improve data access control in information platforms, ensure data security, and accelerate global model training, this article introduced a joint mechanism of differential privacy and FedAvgM (Federated Averaging with Momentum) optimization to enhance privacy protection and improve training efficiency. Firstly, the Laplacian noise mechanism was adopted to prevent user privacy information from being leaked during global model training by adding noise to the dataset, ensuring the privacy of each data access. Then, combined with the FedAvgM optimization algorithm, distributed nodes calculated local model parameters separately and merged these parameters through weighted averaging to reduce the training time of the global model and improve efficiency. Finally, layered encryption technology was adopted to add multiple layers of encryption during data transmission to ensure the security of the transmission link. At the same time, a dynamic permission allocation mechanism was introduced to limit the frequency of access to sensitive data. The experimental results demonstrate that when the privacy budget at ε=20, the privacy protection method of Laplace mechanism still maintains 73% accuracy in data transmission, with a privacy leakage risk of only 0.27. Under the same training epochs, the FedAvgM optimization algorithm achieved a correct data transmission accuracy of 97%. In comparison, the convergence speed increased by 0.29%/min in 1-20 training epochs, showing a faster convergence speed. Under four different noise attack methods, the anti-noise ability of layered encryption is the lowest at 15.4 dB, 18.5 dB, 17.1 dB, and 17.3 dB, respectively. This method effectively improves the problems of privacy leakage risk and low efficiency in traditional data access control.

Optimization of digital back-propagation for coherent optical fiber communication systems using fourth-order Runge-Kutta in the interaction picture method

The digital back-propagation (DBP) is an algorithm that can equalize the chromatic dispersion and nonlinearity in the coherent optical fiber communication system. However, the nonlinear equalization effect of traditional split-step Fourier method (SSFM)-based DBP is limited. This paper replaces the SSFM in DBP algorithm with the fourth-order Runge-Kutta in the interaction picture (RK4IP) method, and employs the Bayesian optimization algorithm (BOA) to optimize the coefficients in RK4IP-based DBP algorithm, then compares it with SSFM-based DBP algorithm, which is also optimized using BOA. The experimental results of 9×100 km standard single-mode fiber (SSMF) 20 Gbaud 16QAM transmission system demonstrate that the coefficient-optimized RK4IP (CO-RK4IP)-based DBP algorithm can achieve the maximum improvement of 0.89 dB in the Q-factor compared to the coefficient-optimized SSFM (CO-SSFM)-based DBP algorithm at equivalent complexity, proving that CO-RK4IP is an effective recursive method for DBP algorithm.

Time optimal trajectory planning of robotic arm based on improved sand cat swarm optimization algorithm

Time optimal trajectory planning of robotic arm based on improved sand cat swarm optimization algorithm

A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection

There are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community detection problem. First, the coding, initialization, and updating strategies of the spider wasp optimization algorithm are discretized to adapt to the community detection problem. Second, the concept of intra-population and inter-population consensus community is proposed. Consensus community is the knowledge formed by the swarm summarizing the current state as well as the past history. By maintaining certain inter-population consensus community during the evolutionary process, the population in the current time window can evolve in a similar direction to those in the previous time step. Experimental results on many artificial and real dynamic networks show that the proposed method produces more accurate and robust results than current methods.

Design of Power Economic Dispatch Method Integrating Differential Evolution and PSO

As energy demand continues to grow and environmental problems become increasingly serious, optimizing the economic dispatch of the power system is crucial to ensuring the sustainability and economic benefits of energy supply. To ensure the safe operation of the power system, reduce power generation costs as much as possible, and develop a method that can adapt to the needs of different power systems, the experiment combines the differential evolution algorithm with the power economic dispatch problem and proposes a method based on improved differential evolution. Electric power economic dispatch method with particle swarm optimization algorithm. The experiment first introduces a moderate interference strategy to appropriately adjust the position of the particles; then combines the local mutation strategy to enhance the searchability of the differential evolution algorithm in the solution space and achieve good economic dispatch. The results show that when running on the F11 test set and F21 test set, when the system iterates to the 26th and 32nd times respectively, the loss function of the method constructed in the experiment begins to have a minimum value and remains stable thereafter. In addition, on the F11 test set, when the number of iterations is 150, this method has a minimum time of 0.153s. While running the loop for the first time on System 1, the total cost of this approach was only $1.01× 104. Through the actual operation of power generation equipment, under the operation of this method, the power system can ensure optimal operating power of each power generation equipment unit based on ensuring optimal cost. It can be seen from the above results that this method provides power system operators and decision-makers with a new tool to help them maximize cost-effectiveness while ensuring system stability and meeting power demand. In addition, the method's superior convergence and stability can effectively improve the solution's accuracy and speed and has strong practicality and promotion value.

Capacity optimization of photovoltaic storage hydrogen power generation system with peak shaving and frequency regulation

To solve the problem of power imbalance caused by the large-scale integration of photovoltaic new energy into the power grid, an improved optimization configuration method for the capacity of a hydrogen storage system power generation system used for grid peak shaving and frequency regulation is proposed. A hydrogen storage power generation system model is established, and the photovoltaic power generation and hydrogen fuel cell power generation is calculated. A two-layer hydrogen storage power generation system capacity optimization configuration model was established, an improved particle swarm optimization algorithm was used to solve the improved hydrogen storage power generation system capacity optimization configuration model, and the capacity optimization configuration results were obtained. The experimental results show that this method can obtain the best capacity allocation result based on local lighting conditions and the cost of the power generation system, whereas the improved particle swarm optimization algorithm can solve the model faster and reduce the cost of the power generation system. After applying this method, the net income of the solar hydrogen storage power generation system has almost doubled.

Application Research of Vision-Guided Grinding Robot for Wheel Hub Castings

The combination of vision and robotic grinding technology provides robots with “visual perception” capabilities that enable them to accurately locate the area to be ground and perform the grinding tasks efficiently. Based on the rough grinding requirements for wheel hub burrs proposed by a casting company, this paper investigates the application of a vision-guided grinding robot in treating burrs on wheel hub castings. First, through vision system calibration, the conversion from pixel coordinate system to robot base coordinate system is implemented, thus ensuring that the subsequently extracted burr point coordinates can be correctly mapped to the robot’s operational coordinate system. Next, the images of the burrs on wheel hub castings are collected and processed. All the burr points are extracted by applying image algorithms. In order to improve grinding accuracy, a height error compensation model is established to adjust the coordinates of the 2D-pixel points; the coordinate error after compensation was reduced by 58.33%. Subsequently, the compensated burr point trajectories are optimized by utilizing an intelligent optimization algorithm to generate the shortest grinding path. Through experimental analysis of the relationship between spindle speed and surface roughness, a grinding trajectory simulation model is constructed, and the simulation results are integrated into the robot system. Finally, actual wheel hub burr grinding experiments are performed to validate the effectiveness and practicality of the proposed solution.

Optimal synchronization of overcurrent relays in active distribution networks using a hybrid genetic-differential optimization algorithm

Optimal synchronization of overcurrent relays in active distribution networks using a hybrid genetic-differential optimization algorithm

Computationally expensive constrained problems via surrogate-assisted dynamic population evolutionary optimization

This paper proposes a surrogate-assisted dynamic population optimization algorithm (SDPOA) for the purpose of solving computationally expensive constrained optimization problems, in which the population is dynamically updated based on the real-time iteration information to achieve targeted searches for solutions with different qualities. Specifically, the population is dynamically constructed by simultaneously considering the real-time feasibility, convergence, and diversity information of all the previously evaluated solutions. The evolution strategies adapted to dynamic populations are designed to arrange targeted search resources for individuals with different potentials. Specifically, for mutation, targeted base solution selection for the top 2 and other center points is designed for emphasizing the exploitation in promising regions; for selection, the search sources arranged on the best and other population individuals are adaptively adjusted with the iteration progresses; for constraint handling, the diversity of infeasible solutions is integrated into the original constraint-domination principle to avoid the locality of only using constraint violation to rank infeasible solutions. For accelerating the convergence, the sparse local search is designed based on update state of the current best solution in which two excellent but non adjacent individuals are used to provide valuable guidance information for local search. Therefore, SDPOA strikes a balance between feasibility, diversity, and convergence. Empirical studies demonstrate that the SDPOA achieves the best performance among all the compared state-of-the-art algorithms, and the SDPOA can obtain new structures with smaller compliance in the design of polyline-based core sandwich structures.

The Application of Microcomputer Systems in Automatic Control Systems

With the continuous advancement and innovation of computer system technologies, the performance of microcomputers has improved significantly. The enhanced computing power of microcomputer hardware provides greater flexibility for the development of various intelligent optimization algorithms. Integrating microcomputers into automatic control systems effectively addresses the systems demands for signal stability, sensitivity, response speed, and manufacturing cost, thereby significantly advancing the practical application of automatic control systems. This paper comprehensively explores the current state and challenges of applying microcomputers in automatic control systems, while providing an in-depth analysis of development trends in this field. It systematically explains the specific improvements microcomputers bring to control systems and how to select appropriate algorithms based on different types of automatic control systems. Moreover, the review highlights the impact of hardware performance limitations of microcomputers on algorithm implementation and discusses potential solutions and directions for future technological advancements. It also examines successful applications and existing issues in representative automatic control systems employing microcomputers. Finally, the study anticipates the development trends of intelligent optimization algorithms for microcomputers in automatic control systems, as well as the emerging challenges and research directions

A Graph Neural Network-Based Approach to XANES Data Analysis.

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction. It turns out to be faster than the traditional XANES fitting method when the simulation approach and XANES optimization algorithm are combined to fit the 3D structure of the given system. The geometric features of the system are included in the weighted message passing block of XAS3Dabs and their importance is investigated. XAS3Dabs model demonstrates superior accuracy in XANES prediction compared to most machine learning models. By extracting graphs constituted by edges related to the absorbing atom, our model reduces redundant information, thereby not only enhancing the model's performance but also improving its robustness across different hyperparameters. XAS3Dabs model can be generalized to simulate the spectra for the systems with the absorber having the designed absorption edge so as to meet the expectations of online data processing. The method is expected to be the key part of the online 3D structure analysis framework for the XAS-related beamlines of high-energy photon source (HEPS) now under construction.

Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Application of Statistical Models in Air Quality Monitoring

Currently, statistical models are vital instruments for examining and forecasting diverse environmental variables, including air quality analysis. With the development of big data, foundational statistical techniquesincluding regression and time series analysisare adapting to manage larger datasets and complex environmental dynamics. This paper examines the recent applications of statistical models used to assess and forecast air quality in Chinese cities based on existing literature and data research. By focusing on various modeling approaches, such as Poisson regression, grey correlation, and neural networks, it discusses the advantages and drawbacks of each model. Furthermore, it examines the enhancement of classical analytical methodologies inside a big data framework to augment the accuracy of air quality analysis and forecasting. The result shows that although datasets have been more extensive, to increase the accuracy of pollution forecasting, diverse data and more excellent computational capabilities are still needed. Advances in machine learning and optimisation algorithms show promise for overcoming these challenges in the future

Intelligent Waste Management Optimization Through Machine Learning Analytics

Waste management presents serious obstacles to making metropolitan regions more habitable. Conventional waste management techniques are not usually optimized, resulting in overflowing bins, wasteful waste collection trips, and various negative environmental effects. This study addresses these challenges by developing an intelligent system integrating the Internet of Things (IoT) and machine learning technologies. This study aims to develop an intelligent waste management system that optimizes waste collection routes and schedules through machine learning. (ML) models and Internet of Things (IoT) powered smart bins. The system utilized Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for data analysis, complemented by dynamic route optimization algorithms. Data collection over 90 days across 47 sites encompassed bin fill levels, battery status, and environmental parameters such as temperature and humidity. Results demonstrated significant operational improvements, with the system achieving 89% accuracy in fill-level prediction and enabling a 35% reduction in collection frequency. Implementation led to a 42% decreased fuel consumption and a 2.4-hour reduction in daily collection times. Commercial zones exhibited 1.8 times higher fill rates than residential areas, while weekend waste generation peaked at 2.1 times weekday. The findings indicate that IoT-ML technology integration substantially enhances urban waste management efficiency through data-driven decision-making. Phased implementation, prioritizing high-waste-volume areas, integrating with existing metropolitan systems, and developing standardized data protocols are recommended. This research contributes to the growing body of evidence supporting smart technology adoption in urban waste management, offering a scalable solution for improved operational efficiency and environmental sustainability.

Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection.

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO's search capabilities. The original PLO's particle collision strategy is replaced with DE's mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE's superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE's effectiveness for both global optimization and feature selection.

Graph coarsening for fugitive interception

The police control room determines where to send available police units to intercept a fleeing fugitive. Models can support the police with decision-making for fugitive interception. The police have, at most, a few minutes to determine an interception strategy. Therefore, a timely calculation of the interception positions is essential to support police interception operations. The number of nodes in the network, each being a crossing where routes of the fleeing suspect can split, greatly contributes to the computation time. Graph coarsening is a promising approach to reduce the complexity of the network, and therefore the computation time. We compare four graph coarsening algorithms on five road networks and assess their impact on computation time and solution quality for the fugitive interception problem. Based on the comparison, we propose and test a new method specifically for fugitive interception. This method, Search Space Representation, improves the quality of the best solutions obtained by the optimization algorithm with up to 12%, improves the reliability of the optimization to find high-quality solutions, and decreases the number of function evaluations required to obtain high-quality solutions to 5000–10,000 depending on the size and complexity of the road network, which is feasible for real-time decision-making. Search Space Representation can be applied to reduce the computation time of other network-based optimization problems.

A novel method of MPPT of photovoltaic power generation system based on MSINGO

Abstract The speed and accuracy of Maximum Power Point Tracking (MPPT) have a significant impact on photovoltaic power generation. In this paper, a novel method is proposed for fast and accurate MPPT of photovoltaic power generation systems. First, the Northern Goshawk Optimization algorithm (NGO) is introduced in the MPPT. Second, a multi strategy is applied to improve the NGO, and MSINGO is proposed. In the MSINGO algorithm, the optimal individual leadership strategy enhances the optimization accuracy and convergence speed, the enhanced Levy flight strategy leads it to escape from the local optimum, and the pinhole imaging learning strategy guides the population towards the global optimum. Finally, MSINGO was applied to MPPT, and a simulation analysis of a single photovoltaic module and photovoltaic array under different working conditions was conducted and compared with other intelligent optimization algorithms. The results show that the proposed method can achieve MPPT with minimal time consumption and better tracking efficiency.

A systematic review of AI-enhanced techniques in credit card fraud detection

The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities.

Integration of Robust Control and Multi‐Objective Metaheuristic Optimization for Improved Stability and Tracking Performance of WIP Systems

ABSTRACTThe Wheeled Inverted Pendulum (WIP) system presents a challenging control problem in the field of control systems due to its inherent instability, non‐linearity, multivariable nature, and under actuation. This paper introduces a novel framework that integrates a robust combined control algorithm with the Inverted Pendulum Multi‐objective Optimization (IPMO) algorithm to improve the stability and tracking performance of WIP systems. The robust control algorithm combines Time‐Varying Sliding Mode Control (TVSMC), Exponential Reaching Law (ERL), and Non‐linear Feedback Control (NFC) to mitigate chattering and enhance stability. The IPMO algorithm utilizes a Solutions Validity Table (SVT) to balance exploration and exploitation in the search for optimal controller parameters. By optimizing the controller parameters, the aim is to minimize tracking errors and improve the overall performance of the WIP system. Extensive simulations are conducted to demonstrate the effectiveness of the proposed framework in enhancing the WIP system performance and minimizing tracking errors. The proposed IPMO algorithm provides decision‐makers with a range of optimal solutions, contributing to the advancement of control techniques for unstable, highly non‐linear, multivariable, and underactuated systems. A supplemental animated simulation of this work is available at https://www.youtube.com/watch?v=PO‐RzmTXemI.