Traditional Optimization Algorithms Research Articles

Research on Transformer Fault Diagnosis Model Based on Improved GOA-SVM

A transformer fault diagnosis model based on the improved grasshopper optimization algorithm-optimized support vector machine (SVM) is proposed to improve the precision of transformer fault diagnosis and avoid the issues that the traditional grasshopper optimization algorithm (GOA) is prone to falling into local optimal solution and slow convergence. Firstly, the grasshopper population is initialized using the elite backward learning strategy to improve the initial population quality and search efficiency. Then, the Sigmoid function is introduced to improve the linear weight decreasing of the grasshopper algorithm into nonlinear weight decreasing to strike a balance between the algorithm’s capacity for both local and global exploration. Finally, the kernel function parameters and penalty coefficients of the SVM are optimized with the improved grasshopper optimization algorithm (IGOA) to establish a model based on dissolved gas analysis (DGA) in oil-based IGOA algorithm-optimized SVM for transformer fault diagnosis model and verify the effectiveness and superiority of IGOA-SVM to identify transformer fault states by comparing with PSO-SVM and GOA-SVM.

Online Weapon-target Assignment based on Distributed Auction Mechanism

To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with centralized architecture within the group and distributed structure between the groups, and the corresponding distributed auction mechanism is constructed to achieve OWTA within the communication range limited. In order to solve the problem that it is difficult to ensure the time consistency of the coordinated attack target, a dubins cooperative path planning based on cooperative particle swarm optimization (CPSO) algorithm is proposed. Particle swarm optimization algorithm is used to adjust the radius of the dubins path of each bomb, so that the uav in the same group can hit the target simultaneously without collision and have the shortest flight range. The simulation results show that the designed distributed auction algorithm takes into account the consumption of attack resources, and quickly redistributes the firepower to the new targets in the dynamic uncertain environment, which ensures the maximization of the execution efficiency of the multi-machine cluster fire allocation task.

An Energy Management Strategy for Hybrid Energy Storage System Based on Reinforcement Learning

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy.

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

HFIST-Net: High-throughput fast iterative shrinkage thresholding network for accelerating MR image reconstruction

Background and ObjectivesCompressed sensing (CS) is often used to accelerate magnetic resonance image (MRI) reconstruction from undersampled k-space data. A novelty deeply unfolded networks (DUNs) based method, designed by unfolding a traditional CS-MRI optimization algorithm into deep networks, can provide significantly faster reconstruction speeds than traditional CS-MRI methods while improving image quality. MethodsIn this paper, we propose a High-Throughput Fast Iterative Shrinkage Thresholding Network (HFIST-Net) for reconstructing MR images from sparse measurements by combining traditional model-based CS techniques and data-driven deep learning methods. Specifically, the conventional Fast Iterative Shrinkage Thresholding Algorithm (FISTA) method is expanded as a deep network. To break the bottleneck of information transmission, a multi-channel fusion mechanism is proposed to improve the efficiency of information transmission between adjacent network stages. Moreover, a simple yet efficient channel attention block, called Gaussian context transformer (GCT), is proposed to improve the characterization capabilities of deep Convolutional Neural Network (CNN,) which utilizes Gaussian functions that satisfy preset relationships to achieve context feature excitation. ResultsT1 and T2 brain MR images from the FastMRI dataset are used to validate the performance of the proposed HFIST-Net. The qualitative and quantitative results showed that our method is superior to those compared state-of-the-art unfolded deep learning networks. ConclusionsThe proposed HFIST-Net is capable of reconstructing more accurate MR image details from highly undersampled k-space data while maintaining fast computational speed.

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

This review examines the methods used to optimize the process parameters of laser cladding, including traditional optimization algorithms such as single-factor, regression analysis, response surface, and Taguchi, as well as intelligent system optimization algorithms such as neural network models, genetic algorithms, support vector machines, the new non-dominance ranking genetic algorithm II, and particle swarm algorithms. The advantages and disadvantages of various laser cladding process optimization methods are analyzed and summarized. Finally, the development trend of optimization methods in the field of laser cladding is summarized and predicted. It is believed that the result would serve as a foundation for future studies on the preparation of high-quality laser cladding coatings.

A chaos opposition‐based dwarf mongoose approach for workflow scheduling in cloud

AbstractCloud computing (CC) systems are a form of public infrastructure that has been in use since its beginnings. In such technologies, clients can access current services according to their requirements without knowing where the service is hosted or how it is supplied, and only pay for the services that they really consume. The CC system faces several challenges. Due to the large range of clients and services offered by this platform, it can be concluded that the scheduling problem and resource usage are its primary issues. In order to successfully balance the three main qualities of service (QoS) criteria: time, cost, and resource usage, researchers were inspired to improve the workflow scheduling (WFS) methods in cloud. Since the randomness of the initial population and the inadequate exploration/exploitation capacity, which has an impact on the quality of the solutions, traditional intelligent optimization algorithms have a poor convergence rate when it comes to resource scheduling. In this research, we suggested a hybridization method combining the Dwarf Mongoose algorithm (DWO) and Chaos opposition based learning algorithm called CO‐DWO in an attempt to develop a more effective optimization algorithm for workflow scheduling issue. The CO‐DWO can be much more advantageous in optimization with a better convergence rate in comparison with other literature techniques.

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.

Finite strain, laminate stress minimization with Newton iteration and time integration

We minimize failure criteria with respect to element-wise fiber orientation in laminae (and laminates) undergoing finite strains. A fully mechanical optimization approach is adopted: the analysis is encapsulated in a Newmark time integration equivalent to Nesterov’s first-order minimization algorithm, with Newton iteration followed by the solution of an adjoint system to obtain analytical sensitivities. This is implemented in our in-house software, Simplas. We consider transversely isotropic elasticity; and hyperelasticity, via the homogenized model of an incompressible Neo-Hookean material filled with cylindrical (fiber-like) pores. Two stress-based criteria are adopted: Tsai–Wu and modified Tsai–Hill. A finite strain solid-shell element, known to be locking-free, is used and here extended to perform the sensitivity operations. Three examples of optimized transversely isotropic elastic composites are shown, exhibiting remarkable advantages when compared with traditional optimization algorithms. One example is dedicated to finding optimal cylindrical void orientation in a hyperelasticity framework. The algorithm works for very high values of deformation. During stress minimization, stiffness can either decrease or increase, and this was observed in the numerical experiments.

Development of hybrid models for shear resistance prediction of grouped stud connectors in concrete using improved metaheuristic optimization techniques

Shear connectors are essential in ensuring a composite action in steel–concrete structures. An accurate estimation of the shear resistance of group stud connectors (GSC) is crucial for the design of steel–concrete composite structures. Overfitting problems arise when the artificial neural network (ANN) is trained using traditional optimization algorithms. However, metaheuristic algorithms maintain the equilibrium between exploitation and exploration in solving this problem. This study combines metaheuristic optimization algorithms, including classical particle swamp optimizer (PSO) and novel improved eliminate particle swamp optimizer (IEPSO) with ANN. The proposed algorithms was used to simulate the shear resistance of GSC. A total of 232 data points of push-out test results were collected. The results show that the ANN-IEPSO with simpler architectures outperforms ANN-PSO and traditional optimization algorithms. The efficacy of the metaheuristic algorithms was benchmarked against four existing design codes, including AASHTO, EC 4, JSCE, and GB50017. The global sensitivity analysis visualizes the relationship between the six predictors and target shear resistance and shows the interaction between the two predictors. The number of studs, stud diameter, and stud spacing are the most critical parameters in predicting the shear resistance of the GSC.

Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

A BP-IPSO Algorithm Suitable for Centralized Thermoelectric Generation System Power Tracking under Nonuniform Temperature Distribution

The power-voltage (P-V) characteristic curve of the centralized thermoelectric generation (TEG) system under nonuniform temperature distribution (NTD) exhibits multiple extreme point characteristics, and the traditional maximum power point tracking (MPPT) algorithm is prone to fall into the local maximum power point (LMPP) and takes a long time to track. This paper designs a BP-IPSO algorithm based on back propagation neural network (BPNN) and improved particle swarm optimization (IPSO) for MPPT. The algorithm firstly utilizes the good nonlinear function fitting ability of BPNN to obtain the fitting curve of the relationship between system control input and power output to establish the TEG array power prediction model. Then, the dynamic learning factor and weight coefficient are introduced into the traditional particle swarm optimization (PSO) algorithm to search the output power prediction model and realize MPPT control. MATLAB/Simulink experiment results show that BP-IPSO algorithm can effectively avoid falling into LMPP, quickly and accurately track the global maximum power point (GMPP), and effectively suppress the oscillation of voltage and power during the tracking process. Especially in the start-up test experiment, compared with perturb and observe (P&O), PSO, and grey wolf optimizer (GWO), the energy generated by BP-IPSO increased by 12.84%, 3.18%, and 4.75%, respectively, which improved the system power generation efficiency.

Power Flow Optimization of a Hybrid Energy System with Salp Swarm Algorithm

Electric energy has become more essential in the recent year for all human activities. Therefore the demand for electricity is increasing to an extreme. The non-conventional energy generation methods are attracting the energy suppliers as its design and implementation is comparatively simple than the conventional energy generation. However, the non-conventional energy sources are widely dependent to the nature. Hence the power supply regularity has become a questionable one for non-conventional energy systems. The design of hybrid power system allows addressing this issue by connecting more than one non-conventional energy system together for making a reliable power supply. To regulate the power supply generation on connecting more than system several optimization algorithms were implemented in the present hybrid energy systems. The proposed work aims to study the performances of the hybrid energy system connected with 3KW wind power generation with each 1KW power generation with solar system and battery backup of using salp swarm optimization algorithm. The experimental work is also extended to prove the efficiency of the proposed algorithm with the traditional particle swarm optimization and genetic algorithms.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed, the deployment of Unmanned Aerial Vehicle (UAV) ad hoc networks can provide people with temporary communication services. Therefore, it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption. In this paper, we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization (AVF-PSO) strategy. In particular, we first introduce the gravity model into the traditional Particle Swarm Optimization (PSO) algorithm so as to increase the probability of full coverage. Then, the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced. Finally, in order to reduce the communication interference between UAVs, we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs. Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches. For example, in the target area of any size, the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%, and 2.0–56.1%, respectively. Moreover, the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm

As a basic industry for national economic development, the power industry is closely related to the overall economic and environmental development of China. At present, China is still dominated by thermal power generation. In order to reduce carbon emissions, promote the realization of the “double carbon” goal, and improve the level of clean energy utilization and the operating efficiency of the power system, a wind-light-water storage complementary power generation system is built, and a mathematical model of multi energy complementation is established. The minimum economic cost and the minimum battery capacity are proposed as the objective functions of system capacity configuration. Then a multi-objective evolutionary algorithm based on Pareto optimal space of the NDWA-GA and the PCA is proposed for optimal capacity allocation of multi energy complementary systems in this paper. Compared with the traditional multi-objective optimization algorithm, the correctness and effectiveness of the proposed method are verified. In addition, according to the actual research object, the optimal capacity configuration of the multi energy complementary system is given, which can guide the production and has an important promotion significance for energy saving and emission reduction.

Image segmentation of Intracerebral hemorrhage patients based on enhanced hunger Games search Optimizer

Medical diseases seriously affect human life and health, and a segmentation model that can effectively support doctors in making the correct diagnoses of medical disease images is needed. Multi-threshold image segmentation is famous for its simplicity and ease of implementation, but the choice of its threshold combination affects its performance, and traditional optimization algorithms fall into local optimality with significant time consumption when solving such problems. Therefore, metaheuristic algorithms have been applied to this field, but many have drawbacks, such as slow convergence, easy prematureness, and unbalanced performance when performing threshold selection. For instance, the Hunger Games Search (HGS) algorithm proposed last year is unsatisfactory regarding convergence accuracy and speed. Hence, an improved HGS (SCHGS) is proposed by combining the slime mould position update mechanism and chaotic optimal solution variation. The slime position update mechanism has a powerful exploration capability, which can help HGS increase the exploration of the search space and find the optimal solution as much as possible. On the other hand, the chaotic optimal solution variation strengthens the algorithm's local exploitation ability, which can effectively avoid falling into the local optimum. The experimental results on benchmark test functions indicate that the convergence performance of SCHGS is improved by 54% compared with the original algorithm, and there is a more obvious advantage in the convergence speed. In the application of threshold selection in brain hemorrhage image segmentation, the performance of the suggested method also improves by 0.08%, 0.55%, and 0.29% according to different evaluation metrics (FSIM, PSNR, and SSIM), further demonstrating the effectiveness of SCHGS in solving image segmentation problems.

Health Indicators Identification of Lithium-Ion Battery From Electrochemical Impedance Spectroscopy Using Geometric Analysis

Electrochemical impedance spectrum of lithium-ion battery changes regularly with cycling, and is an effective tool for analyzing aging. However, due to the anomalous diffusions and non-exponential effects in battery, EIS-based model is generally identified in complex and time-consuming ways, which limits its online application. In this paper, a parameter identification method for EIS-based model is proposed using geometric analysis. By fitting the impedance spectrum at intermediate frequencies with two depressed semicircles, the parameters of EIS-based model are directly calculated, greatly reducing the computational complexity. Compared with the traditional optimization algorithms, the indexes of the proposed method are optimal, considering running time, file size and goodness of fit. Furthermore, six model parameters are identified based on EIS at different temperatures, states of charge and aging cycles, and their relationship with capacity decay is analyzed. The results show that the charge transfer resistance always has an excellent linear correlation with capacity decay, and the correlation coefficient exceeds 0.94 regardless of temperature and state of charge. It can be used as health indicator of battery. Moreover, one fractional-order parameter has a good correlation with the capacity only at high temperature.

Hybrid Optimization Algorithm for Multi-level Image Thresholding Using Salp Swarm Optimization Algorithm and Ant Colony Optimization

The process of identifying optimal threshold for multi-level thresholding in image segmentation is a challenging process. An efficient optimization algorithm is required to find the optimal threshold and various nature inspired; evolutionary optimization algorithms are presented by the research community. However, to improve the performance in finding optimal threshold value and minimize the error, reduces the searching time a hybrid optimization algorithm is presented in this research work using salp swarm optimization and ant colony optimization algorithm. The ant colony optimization algorithm is used to enhance the exploration and exploitation characteristics of salp swarm optimization in finding optimal threshold for the given image. Experimentation using standard images validates the proposed model performance in comparison with traditional optimization algorithms like moth flame optimization, whale optimization algorithm, grey wolf optimization, artificial bee colony and bee foraging optimization algorithms. Proposed hybrid optimization outperformed in all parameters compared to traditional optimization algorithms and provides better optimal thresholds for the given input image.

Quantum Neural Network for Quantum Neural Computing.

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.

Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection

Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Plant Algorithm (WWPA) further enhances performance, as exemplified by the hybrid WWPA-CNN model, exhibiting an impressive accuracy of 97.28%. These findings highlight the promising synergies between traditional optimization algorithms and advanced neural networks, showcasing the potential for innovative developments in traffic monitoring systems and broader applications within computer vision. The statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, robustly underscore the significance of this integrated approach. As the research contributes to the evolution of traffic monitoring systems, these insights provide a solid foundation for advancements in the field, fostering innovation and shaping the future landscape of computer vision applications.