Improved Hybrid Algorithm Research Articles

Port environmental path planning based on key obstacles

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper’s algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment.

Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm.

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective.

ICSOMPA: A novel improved hybrid algorithm for global optimisation

ICSOMPA: A novel improved hybrid algorithm for global optimisation

Enhancing long-term river water quality prediction: Construction and validation of an improved hybrid model

To prevent eutrophication of river caused by excessive inputs of nutrients, accurately predicting these nutrient concentrations is of utmost importance. However, long-term accurate predictions of nutrients concentrations remain a challenge. In this study, an improved hybrid algorithm combining adaptive variational mode decomposition and multi-functional recurrent fuzzy neural network (AVMD-MFRFNN) was proposed. Firstly, whale optimization algorithm (WOA) was developed to identify the optimal number of modes for variational mode decomposition (VMD). Then VMD was employed to decompose input data into intrinsic mode fractions (IMFs), benefiting for reducing the noise effect and non-stationarity of data. Then, MFRFNN with the capability of characterizing complex data was developed to train and test the IMFs. The proposed AVMD-MFRFNN achieved impressive results with the root mean square errors (RMSEs) of 0.036 mg/L, 0.758 mg/L and 1.435 mg/L for predicting total phosphorus (TP), chemical oxygen demand (CODMn) and total nitrogen (TN) after 168 hours. These results signify a decrease of 5.3%, 14.1% and 30.1% compared to the second-best compared method. Moreover, this study demonstrates the potential that integrating the proposed AVMD with other methods can enhance the modeling performance of time series. Furthermore, sensitivity analysis results further validate the effectiveness of WOA in optimizing VMD parameter. Finally, the applicability of AVMD-MFRFNN was confirmed by the data from another river section. These results provide new insights and technical support into the management and preservation of river water environment.

Selective harmonic elimination in PUC‐5 multilevel inverter using hybrid IGWO‐DE algorithm

AbstractThe use of a multilevel inverter has received significant attention in recent years due to its numerous advantages. Because of the widespread use of multilevel inverters in industries and applications that require a wide range of voltages, achieving high‐quality voltage has presented a number of challenges. Many studies have been carried out to address the problem of unwanted harmonics in multilevel inverters. The inverter switching can be done at a low frequency using the Selective Harmonic Elimination (SHE) technique, and the unwanted harmonics can be significantly reduced; however, the issue with SHE is solving the transcendental equations and determining the optimum switching angle. To address this problem, a new and improved hybrid algorithm is proposed that combines two evolutionary algorithms, gray wolf optimization (GWO) with an improved and new convergence factor and Differential evolution (DE) with a dynamic scaling factor using a crossover operator. In this paper, the optimum switching angles for a packed U cell 5‐level inverter are estimated using the proposed algorithm for distinct modulation index values, and simulation results are compared with different algorithms. The simulation result of the proposed algorithm, IGWO‐DE is confirmed through experiment.

Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.

Image based agorithm for automatic generation of chinese couplets

Chinese couplet is one of the important forms of expression in Chinese and even world literature, with its own unique charm and beauty. In order to meet the needs of users who only need one image to obtain corresponding couplets, realize the function of computer automatically writing couplets with images, and improve the literary expression ability of couplets to images, this paper proposes an image based intelligent generative model of couplets. The model first outputs corresponding descriptions based on image extraction features, and then extracts keywords through an improved hybrid algorithm according to the descriptions. Then, based on the keywords, the Chinese GPT-2 model automatically expands the first line of a couplet, and finally generates the second line of a couplet from the first line of a couplet through the encoding and decoding framework. Through experiments, it has been shown that the generated couplets of the model meet the requirements for image description, and the effectiveness of the model has been confirmed by manual evaluation results.

Research on the Vehicle Steering and Braking Stability Region

Solving the stability region in the plane motion of vehicles has become a hot research topic in vehicle handling stability under extreme conditions, but there is still a lack of research on the stability region under steering and braking conditions. In this paper, a five-degree-of-freedom (5DOF) nonlinear dynamic model of a vehicle with braking torque introduced is established, and the model is transformed into an equivalent system by using the D’Alembert principle. Then, the equilibrium points of the equivalent system are solved by using an improved hybrid algorithm combining the genetic algorithm (GA) and sequential quadratic programming (SQP) method. According to the bifurcation characteristics of the equilibrium points, the boundary of the stability region at the given initial longitudinal velocity is determined, and the three-dimensional stability region is fitted. Finally, the stability region of the equivalent system and the original system are analyzed by the energy dissipation method, and the stability region determined by the equilibrium point bifurcation method is verified by simulation. The results show that as the braking torque increases, the number of equilibrium points increase to three from one, the equilibrium bifurcation method proposed in this paper can effectively solve the stability region of the equivalent system, and the solution results are consistent with the original system stability region. When the limited braking torque is 500 N·m and the initial longitudinal velocity increases from 30 m/s to 50 m/s, the absolute value of the front wheel steering angle at the boundary point changes from less than 0.02 rad to more than 0.02 rad.

OptiMD-3D DCNN: A Framework for Restoring the Haze-Free Images by Image Dehazing Techniques Using Heuristic Approach of Adaptive Lifting Wavelet Transform

The recently implemented illustrative techniques are focused to restore haze-free images by using learning approaches and also physical models. Managing finer details of the images when completely removing the haze is a complicated task especially in performing the hazing on single images. Generally, hazy images comprise distorted colors, poor contrast, and lower visibility, which lead to lower efficiency in the recognition, detection of objects, and classification of images. The main aspect of the proposed work is to restore haze-free images without any degradation in image quality. Initially, the required raw hazy images are acquired from the benchmark data sources. Further, these images undergo the decomposing stage, which is accomplished by adopting the Adaptive Lifting Wavelet Transform (ALWT), where the wavelet levels are optimized by using the improved hybrid algorithm of the Modified Tunicate Galactic Swarm Optimization (MTGSO). Once the decomposed images are obtained, Optimal Multiscale Dilation assisted 3D Deep Convolutional Neural Network (OptiMD-3D DCNN) is developed for the image dehazing task, in which the hyperparameters are optimally tuned using a hybrid MTGSO algorithm. At last, the effectiveness is examined and guaranteed that the recommended scheme shows higher performance in estimating with the classical methods.

Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

Cloud computing is the technology that is currently used to provide users with infrastructure, platform, and software services effectively. Under this system, Platform as a Service (PaaS) offers a medium headed for a web development platform that uniformly distributes the requests and resources. Hackers using Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks abruptly interrupt these requests. Even though several existing methods like signature-based, statistical anomaly-based, and stateful protocol analysis are available, they are not sufficient enough to get rid of Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks and hence there is a great need for a definite algorithm. Concerning this issue, we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis, Spearman coefficient, and mitigation technique. It can easily differentiate common traffic and attack traffic. Not only that, it greatly helps the network to distribute the resources only for authenticated requests. The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99% for the numerous DoS attack as well as DDoS attacks.

Improved SOSK-Means Automatic Clustering Algorithm with a Three-Part Mutualism Phase and Random Weighted Reflection Coefficient for High-Dimensional Datasets

Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, metaheuristic algorithms are hybridized with K-means to extend the capacity of K-means in handling automatic clustering problems. In this study, we proposed an improved version of an existing hybridization of the classical symbiotic organisms search algorithm with the classical K-means algorithm to provide robust and optimum data clustering performance in automatic clustering problems. Moreover, the classical K-means algorithm is sensitive to noisy data and outliers; therefore, we proposed the exclusion of outliers from the centroid update’s procedure, using a global threshold of point-to-centroid distance distribution for automatic outlier detection, and subsequent exclusion, in the calculation of new centroids in the K-means phase. Furthermore, a self-adaptive benefit factor with a three-part mutualism phase is incorporated into the symbiotic organism search phase to enhance the performance of the hybrid algorithm. A population size of 40+2g was used for the symbiotic organism search (SOS) algorithm for a well distributed initial solution sample, based on the central limit theorem that the selection of the right sample size produces a sample mean that approximates the true centroid on Gaussian distribution. The effectiveness and robustness of the improved hybrid algorithm were evaluated on 42 datasets. The results were compared with the existing hybrid algorithm, the standard SOS and K-means algorithms, and other hybrid and non-hybrid metaheuristic algorithms. Finally, statistical and convergence analysis tests were conducted to measure the effectiveness of the improved algorithm. The results of the extensive computational experiments showed that the proposed improved hybrid algorithm outperformed the existing SOSK-means algorithm and demonstrated superior performance compared to some of the competing hybrid and non-hybrid metaheuristic algorithms.

Optimization of stamping process parameters based on an improved particle swarm optimization–genetic algorithm and sparse auto-encoder–back-propagation neural network model

Stamping is a very important manufacturing process. To optimize the process parameters, a hybrid surrogate model based on the back-propagation neural network and sparse auto-encoder is proposed and compared with classical surrogate models to verify its reliability. Furthermore, the hybrid improved particle swarm optimization–genetic algorithm, based on chaos theory, is proposed and compared with other algorithms. A double-C part is used as an engineering example to verify the proposed method. The Latin hypercube sampling method is used for sampling and the response value is obtained by AutoForm simulation software. On this basis, the hybrid surrogate model is used to establish the mapping relationship between the forming quality of the double-C part and the stamping process parameters. The optimal stamping process parameters are obtained through the improved hybrid algorithm. The results demonstrate that the wrinkling of the optimized double-C part is significantly reduced and the forming quality is improved.

Research on collaborative scheduling of multiple projects of prefabricated building based on the niche genetic-raccoon family optimization algorithm

Buildings where part or all of the components are prefabricated in a factory, then transported to the construction site and assembled through reliable connections, are called prefabricated buildings (PB). Considering the regional differentiation and asynchronization scheduling characteristics of PB, it is difficult for project managers to schedule and optimize construction resources - including manpower/equipment for parallel assembly projects. The purpose of this paper is to solve the multi-projects resource optimization problem under the condition of prefabricated buildings. Therefore, this paper establishes a bilevel multi-objectives model composed of project layer and work layer, which aims to shorten project duration, reduce cost, and maximize robustness. Based on this, a hybrid niche genetic-raccoon family optimization (NG-RFO) algorithm is proposed, equipped with better population diversification effect of niche genetic (NG) and strong local search ability of raccoon family optimization algorithm (RFOA). Moreover, the performance of the improved hybrid algorithm has been compared with genetic algorithm (GA) and RFOA. The actual data of a prefabricated building project group in Hebei Province has been used. The results show that the algorithm is a feasible and efficient method for obtaining a satisfactory resource scheduling scheme in limited time, which is obviously superior to GA and RFOA. This paper provides a reference for project managers to make reasonable and efficient progress plans in an uncertain environment.

Two-sided matching decision-making model for complex product system based on life-cycle sustainability assessment

Since the different matching schemes between manufacturing tasks and services have a great influence on the life cycle sustainability of complex product system (CoPS), the strategy used to find cost-effective, environmentally friendly, and socially acceptable matching results is essential for sustainable development. This paper proposed a novel two-sided matching model for CoPS based on life-cycle sustainability assessment (TSMM-CoPS-LCSA). First, the two-sided matching model based on the life-cycle sustainability assessment of manufacturing tasks and services for CoPS is presented. Then, a bi-level decision-making method is proposed, where learning and synergy effect are considered to ensure stability in the matching process. Sequentially, to facilitate the optimization solution, the Karush-Kuhn-Tucker (KKT) conditions are adopted to convert the bi-level model to a single model which aims to minimize the total cost, environmental impact, and social impact. Subsequently, for solving this nonlinear and complex multi-objective decision-making model, an improved hybrid algorithm that integrated Tabu Search and non-dominated sorting genetic algorithm II (TS-NSGA-II) is applied to solve the above model. Finally, a case study of CoPS manufacturing project is illustrated to demonstrate the effectiveness and superiority of the proposed TSMM-CoPS-LCSA model. The results indicate that the proposed method can find a better services scheme in line with actual demand, which provides a guiding significance for promoting the sustainable development of the manufacturing industry.

Evaluation Method of Industrial Efficiency of Green Manufacturing Enterprises Based on Machine Learning

The cultural construction in the process of industrialization is intertwined with the culture required by the development of green manufacturing industry, which has become the growth point of economic construction and the new trend of economic development. Efficiency is the basis for the development of various industries. If we do not improve efficiency, industrial development will cause waste of resources and environmental pollution. Therefore, this study proposes a new evaluation method of green industrial manufacturing efficiency. The proposed method suggests cultural enterprises. In addition, this study examines the cultural green manufacturing industry productivity of cultural enterprises and combines it with machine learning. The excellent performance of neural network in prediction makes it possible to predict the efficiency of green manufacturing industry of cultural enterprises. Genetic algorithm is also proposed to optimize BP network. This algorithm is easy to operate and requires few parameters. In the process of finding the optimal solution, the optimal individual in the group can be used to control the iterative process. The particle swarm optimization algorithm is improved and combined with genetic algorithm to get an improved hybrid algorithm. BP network is optimized, and an improved BP network prediction model is established to evaluate the efficiency of green manufacturing industry of cultural enterprises. A large number of experiments have proved the effectiveness and reliability of this method. Separate simulations and results are presented to verify the effectiveness of the proposed model.

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm.

Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.

Maximization of the fundamental frequency of the FG-CNTRC quadrilateral plates using a new hybrid PSOG algorithm

This research work presents the fundamental frequency optimization of the functionally graded carbon nanotube reinforced composite (FG-CNTRC) quadrilateral plates using an improved hybrid algorithm of the particle swarm optimization and genetic (PSOG) algorithm. The objective function is to maximize the fundamental frequency of the FG-CNTRC quadrilateral plates and the design variables are a set of the discrete values of the fiber orientations in the layers. The natural frequency of the plates is obtained by using the cell-based smoothed discrete shear gap method. The several comparative numerical examples are investigated to show the efficiency of the proposed hybrid PSOG algorithm. It is demonstrated in the numerical examples that this design method can provide with the accurate optimum solutions for the stacking sequences of the vibrating FG-CNTRC quadrilateral plates.

Exploration on the Application Path of College English MOOCS Teaching under the Background of Internet of Things

While the development of information and communication technology has brought great changes to social industrial structure and economic development, it has also reshaped people's cognition and thinking modes to a great extent and has paid attention to the field of education under the guidance of technology. The reform of education is taking place. According to the background of the Internet of things, the teaching quality evaluation index is reconstructed with the help of principal component analysis, support vector machine, and fuzzy algorithms, and the application performance and practical results of the improved teaching path are analyzed. The results show that the evaluation accuracy of the improved hybrid algorithm for teaching quality reaches more than 90%, and its evaluation time is far less than that of the SVM algorithm (10.23 s), and it has a good application effect on the evaluation of teaching quality. The increase of students' satisfaction with the improved teaching quality has reached 5.8%, and their professional skills have improved by 6–25 points. After expert evaluation, it is found that the maximum satisfaction frequency of teaching content and teaching effect can reach 96.7% and 91.3%, respectively. Reconstructing the index system of the English path under the background of the Internet of things can better grasp students' learning needs and integrate multiple methods to provide new research ideas for follow-up English teaching.

A Chaotic Fish Swarm Algorithm-Based Model for Assessing the Mental Health Status of Older Adults

In this paper, the chaotic fish swarm algorithm is used to conduct in-depth research and analysis on the assessment of the mental health of the elderly. Firstly, the principle, search method, and characteristics of the harmonic search algorithm are analysed, and it is proposed to use the excellent local fine-tuning ability of the harmonic search algorithm to improve the local search accuracy of the artificial fish swarm algorithm. Then, the concept of chaos factor is introduced to improve the global search of the artificial fish swarm algorithm efficiency, using its global search capability without repeated traversal to form a new hybrid fish swarm algorithm. The comparison of experimental results shows that the improved algorithm can effectively guide the robot to avoid obstacles and quickly find the best path or a better path. The improved hybrid algorithm is more efficient and reliable than other algorithms in path planning and can handle more a complex environment model. When considering sample selection bias, ordinary least squares (OLS) regression may underestimate the extent to which social participation affects the mental health of older adults. Further research found that there is heterogeneity in the influence of social participation on the mental health of the elderly. In addition, different types of social participation have different effects on the mental health of the elderly. Simply making friends, physical exercise, and recreational participation in social activities can significantly improve the mental health of the elderly. The improvement is the strongest.

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

The losses in electrical power systems are a great problem. Multiple methods have been utilized to decrease power losses in transmission lines. The proper adjusting of reactive power resources is one way to minimize the losses in any power system. Reactive Power Optimization (RPO) problem is a nonlinear and complex optimization problem and contains equality and inequality constraints. The RPO is highly essential in the operation and control of power systems. Therefore, the study concentrates on the Optimal Load Flow calculation in solving RPO problems. The Simple Particle Swarm Optimization (PSO) often falls into the local optima solution. To prevent this limitation and speed up the convergence for the Simple PSO algorithm, this study employed an improved hybrid algorithm based on Chaotic theory with PSO, called Chaotic PSO (CPSO) algorithm. Undeniably, this merging of chaotic theory in PSO algorithm can be an efficient method to slip very easily from local optima compared to Simple PSO algorithm due to remarkable behavior and high ability of the chaos. In this study, the CPSO algorithm was utilized as an optimization tool for solving the RPO problem; the main objective in this study is to decrease the power loss and enhance the voltage profile in the power system. The presented algorithm was tested on IEEE Node-14 system. The simulation implications for this system reveal that the CPSO algorithm provides the best results. It had a high ability to minimize transmission line losses and improve the system's voltage profile compared to the Simple PSO and other approaches in the literature.