Improved Genetic Algorithm Research Articles

Model and algorithm for augmenting logistics network resilience with hybrid facilities and robust strategies

This paper introduces a mixed-integer nonlinear programming (MINLP) model aimed at designing a dependable closed-loop supply chain that can function effectively despite disruptions. The model incorporates several strategies to reduce the risks associated with random facility disruptions. First, the concept of p-robustness criterion is employed, which sets upper bounds on the cost in various disruption scenarios to protect against disruptions. To accommodate for the varying levels of reliability of facilities, the network consists of two categories of infrastructure: reliable and defective hybrid infrastructure. Thirdly, a sharing strategy is proposed to mitigate for the capacity loss at unreliable facilities by permitting transportation of products from reliable facilities to unreliable facilities. Additionally, an improved genetic algorithm (IGA) is devised for the MINLP model efficiently. The efficacy of the model and the tailored algorithm is evaluated by means of computational experiments. Finally, a sensitivity analysis is ultimately performed to furnish managers with meaningful insights. For example, the sharing strategies demonstrate a significant decrease of total cost. This paper contributes to enhancing resilience in logistics networks, ensuring their reliable performance even in the face of disruptions.

Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA

Assessing project portfolio risk (PPR) is essential for organizations to grasp the overall risk levels of project portfolios (PPs) and realize PPR mitigation. However, current research is inadequate to effectively assess PPR, which brings challenges to managing PPR. In this context, the purpose of this study is to develop a PPR assessment model via an enhanced backpropagation neural network (BPNN). First, PPR assessment criteria considering project interdependencies are determined. Second, fuzzy logic is used to obtain original data for assessment criteria. Principal component analysis (PCA) is then employed to reduce the dimensionality of assessment criteria and derive the input and output of BPNN. Third, an improved genetic algorithm (IGA) is designed to optimize the initial weights and thresholds of BPNN. On this basis, the PCA-IGA-BPNN assessment model is constructed, followed by training and testing, possessing a test accuracy of 98.6%. Finally, comparison experiments are conducted from both internal and external perspectives. For internal comparison, the proposed model yields less mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) than PCA-GA-BPNN, IGA-BPNN, PCA-BPNN and BPNN and offers the largest convergence speed (γ). As for external comparison, the presented model produces lower MAPE, MSE, and RMSE than Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) and has the largest coefficient of determination (R2). Results indicate that the established model performs more satisfactorily in assessing PPR. This research enriches PPR assessment methods and provides managers with a useful tool to evaluate PPR.

An improved genetic algorithm with modified critical path-based searching for integrated process planning and scheduling problem considering automated guided vehicle transportation task

Integrated process planning and scheduling (IPPS) can take advantage of the complementary attributes of process planning and shop scheduling to obtain better production schemes and process routes improving the whole performance of the manufacturing system. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. This paper investigates an IPPS problem considering AGV transportation task (IPPS_T). Compared with the original IPPS, IPPS_T addresses not only the process selection, operation sequencing, and machine selection but also the transportation task assignment of the AGVs. Therefore, it is much more difficult than the IPPS problem which has already been proven to be NP-hard. The paper proposes an integrated encoding method to improve the integration of the manufacturing system by representing the process route, shop scheduling scheme, and transportation task assignment plan simultaneously in one individual. This paper designs an improved genetic algorithm (IGA) combining a critical path-based neighborhood searching strategy which can ensure the effectiveness of local search on both AGVs and machines. The numerical experiments with different numbers of AGVs are conducted on the open instances which are extended from the well-known Kim benchmark. The results obtained by the IGA show significant advantages proving the effectiveness of the proposed encoding method and critical path-searching strategy.

A Novel Fusion Method for State-of-Charge Estimation of Lithium-Ion Batteries Based on Improved Genetic Algorithm BP and Adaptive Extended Kalman Filter

The lithium-ion battery is the power source of an electric vehicle, so it is of great significance to estimate the state of charge (SOC) of lithium-ion batteries accurately to ensure vehicle safety. To improve the accuracy of the parameters of the equivalent circuit model for batteries, a second-order RC model for ternary Li-ion batteries is established, and the model parameters are identified online based on the forgetting factor recursive least squares (FFRLS) estimator. To improve the accuracy of SOC estimation, a novel fusion method, IGA-BP-AEKF, is proposed. Firstly, an adaptive extended Kalman filter (AEKF) is used to predict the SOC. Then, an optimization method for BP neural networks (BPNNs) based on an improved genetic algorithm (IGA) is proposed, in which pertinent parameters affecting AEKF estimation are utilized for BPNN training. Furthermore, a method with evaluation error compensation for AEKF based on such a trained BPNN is proposed to enhance SOC evaluation precision. The excellent accuracy and stability of the suggested method are confirmed by the experimental data under FUDS working conditions, which indicates that the proposed IGA-BP-EKF algorithm is superior, with the highest error of 0.0119, MAE of 0.0083, and RMSE of 0.0088.

Research on HMCVT Parameter Design Optimization Based on the Service Characteristics of Agricultural Machinery in the Whole Life Cycle

The HMCVT (Hydro-mechanical Continuously Variable Transmission) can realize continuously variable speed in a large range and transmit high power through power diving, and thus, it is widely applied in agricultural machineries, such as tractors. The engineering technology requirements of precision agriculture and intelligent agriculture have put forward higher requests for the research and development of HMCVT. In order to further improve the performance of HMCVT in the design stage and match the service characteristics of agricultural tractors in the whole life cycle, this paper proposes a new HMCVT optimization design method which mainly consists of 5 steps. The proposed method takes the regular and continuous transmission ratio variation, matching the working requirements in the whole life cycle of agricultural tractors and transmission efficiency maximization as comprehensive objectives. The improved genetic algorithm (I-GA) is applied. The HMCVT efficiency characteristic model combines the characteristic efficiency model of the hydraulic system, which has been verified, and the theoretical calculation model based on the engaging power method. By analyzing the statistical data of the tractor speed operating frequency in the whole life cycle, the proposed method is used to determine the weight of each hydro-mechanical power dividing stage (i.e., HM stage) service time. In the design process, the HMCVT efficiency is integrated with the displacement ratio so as to realize the evaluation of the maximum efficiency in the whole life cycle of the tractor. Research results show that the proposed method can improve HMCVT performance significantly, and the mean error between speed regulating characteristics and expected value in design is 1.04% or so. The HMCVT service efficiency in the whole life cycle of the tractor has been improved by 19.93%. The research in the paper offers a valuable reference for getting the law of HMCVT characteristics and performance improvement design of tractors.

Sliding Mode Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor Based on Improved Genetic Algorithm

Sliding mode control has been widely used to control permanent magnet synchronous motors (PMSM). However, the parameters of the sliding mode controller are difficult to be tuned, which makes the control performance of PMSM hard to be improved. A nonlinear sliding mode control method that integrated a nonlinear reaching law (NRLSMC) and extended state observer (ESO) is proposed in this paper, whose parameters are tuned by an improved genetic algorithm (IGA). The control performance of the nonlinear reaching law in the nonlinear sliding mode controller is analyzed, whose stability is verified based on the Lyapunov theorem. An extended state observer is integrated into the above controller to further improve the anti-interference capability, and compensate for the observed external disturbance of the system into the speed controller in sliding mode. The optimal parameters of the above sliding mode control are tuned by IGA combined with the system speed loop model. The performance of the proposed controller is numerically simulated in MATLAB/Simulink and verified in a control system rapid control prototype (RCP) experimental platform built based on dSPACE 1202. Numerical simulation and experimental results show that the proposed controller can make the PMSM control system with the advantages of no overshoot, fast response, and strong robustness.

Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

Production scheduling in a knitting workshop is an important method to improve production efficiency, reduce costs and improve service. In order to achieve a reasonable allocation of parallel machines as well as cooperation between different machines within the workshop, thereby ensuring the optimal scheduling of production plans, this paper proposes a scheduling method using an improved genetic algorithm (IGA) based on tabu search. Firstly, the production scheduling model of a knitting workshop is established. Secondly, an IGA based on the minimum processing time rule, the priority idle machine rule and the production order ranking code is used to optimize the solution. Finally, an experiment platform for knitting workshop production is built to verify the proposed scheduling method. The experimental results show that the proposed IGA based on tabu search performs well in terms of preconvergence speed, global search capability and local search capability. The IGA converges faster than the traditional genetic algorithm by about 25%, reduces the redundancy time of scheduling, meets the production requirements of the knitting intelligent workshop and has a good reference value for promoting the intelligent development of knitting production.

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively.

Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods

Objectives: To identify a new method for early detection and classification of Heart Diseases (HD); to improve the accuracy of the results by employing I-FCMML (Improved Fuzzy C-Means Machine Learning) and 2L-C (Two-Level Classifier) models along with the I-GA (Improved Genetic Algorithm) methods. Methods: The I-FCMML algorithm is utilized for feature selection and extraction. Machine learning techniques such as Ensembled Random Forest method (ERFM) and Robust Gradient Boost Method (RGBT) are employed to predict the likelihood of HD and 2L-C, I-GA is used to classify and detect the HD at a premature stage based on features like age, gender, blood pressure, etc. IFCMML with 2L-C & I-GA extracts all the features from the dataset (Cleveland HD dataset from the Cleveland Clinic Foundation, Ohio, USA) which includes 303 observations, 14 features and selects the suitable function to perform disease classification and detection with high accuracy. To evaluate the performance of the proposed method, MATLAB is used for implementation. The results are compared with existing algorithms such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA. Findings: Early HD detection and classification is achieved with 96.02% accuracy, 95.80% sensitivity, 94.76% specificity, 95% precision, 94% recall, 0.90 True Positive, 0.87 True Negative, and 94.13% F-Score to detect and classify the HD in a robust manner, which is comparatively high than the existing methods. Novelty: According to the findings of the comprehensive study, the proposed new method I-FCMML with 2L-C & I-GA has the potential to provide accurate and competent detection and classification of HD at an early stage, which could help for timely treatment and management of HD patients, and it also outperforms the existing methods such as 3P-ANN, ANNFAHP, ADWFS, EDSS, and FE-PCA. Keywords: Heart Disease Detection; Classification Algorithm; Genetic Algorithm; Fuzzy C-Means ML; Image Processing

Parallel distributed compensation scheme for chaotic masking system via Rivest cipher 4 stream cipher

AbstractThis study presents a design methodology for Takagi‐Sugeno (T‐S) fuzzy models‐based secure communications in multiple time‐delay chaotic (MTDC) systems with Rivest cipher 4 (RC4) algorithm. The main advantage of the RC4 algorithm is that the key length does not affect the encryption and decryption speeds. To block a hacker from stealing a message, this study proposes increasing the complexity of an encrypted message (ciphertext) using a method of double encryption using the RC4 algorithm and chaotic masking. As a result, this more secure communications system can effectively defend the encrypted message (ciphertext). First, the RC4 algorithm and a key are used to encrypt the initial message (plaintext). To enhance security, the encrypted message (ciphertext) is then converted into an encrypted signal, which is a double encryption using chaotic masking. Additionally, an improved genetic algorithm (IGA) is adopted in this study, it can seek better feedback gains of fuzzy controllers to speed up the synchronization. In accordance with the IGA, a model‐based fuzzy controller is synthesized to exponentially stabilize the error systems so that the trajectories of the slave systems can approach those of the master systems. Finally, a numerical example with simulations is provided to illustrate the effectiveness of the proposed method.

An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem.

In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.

Privacy Protection Method for Cellular Signaling Data Based on Genetic Algorithm

Cellular signaling data (CSD) is a new data source of intelligent transportation and has great potential in the study of human mobility. However, research recently has shown that the privacy of individual trajectories of travelers can be easily leaked due to their unique travel patterns, which will lead to the disclosure of a traveler’s trajectory privacy. To address this issue, the risk of privacy leakage in a CSD data set was quantitatively measured due to reidentification attacks. To reduce the risk of trajectory privacy leakage, a spatial generalization method based on an improved genetic algorithm (IGA) was proposed. We transformed the base station aggregation problem that meets the k-anonymity requirement into a shortest path problem. The data utility loss after privacy preservation was evaluated based on origin–destination (OD) flow analysis of traffic analysis zones (TAZs). Finally, the relationships between privacy preservation and data utility were revealed. In all of the cases, more privacy protection corresponded to less data utility. The discovered relationships provide useful guidance for data publishers on how to choose the right tradeoff between privacy protection and data utility when publishing or sharing such sensitive data.

A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks

The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%.

Research on Transmission Task Static Allocation Based on Intelligence Algorithm

Transmission task static allocation (TTSA) is one of the most important issues in the automatic management of radio and television stations. Different transmission tasks are allocated to the most suitable transmission equipment to achieve the overall optimal transmission effect. This study proposes a TTSA mathematical model suitable for solving multiple intelligent algorithms, with the goal of achieving the highest comprehensive evaluation value, and conducts comparative testing of multiple intelligent algorithms. An improved crossover operator is proposed to solve the problem of chromosome conflicts. The operator is applied to improved genetic algorithm (IGA) and hybrid intelligent algorithms. A discrete particle swarm optimization (DPSO) algorithm is proposed, which redefines the particle position, particle movement direction, and particle movement speed for the problem itself. A particle movement update strategy based on a probability selection model is designed to ensure the search range of the DPSO, and random perturbation is designed to improve the diversity of the population. Based on simulation, comparative experiments were conducted on the proposed intelligent algorithms and the results of three aspects were compared: the success rate, convergence speed, and accuracy of the algorithm. The DPSO has the greatest advantage in solving TTSA.

Planning of takeoff/landing site location, dispatch route, and spraying route for a pesticide application helicopter

Relying on the pilot's flight experience to plan a temporary helicopter takeoff/landing site location, dispatch routes, and spraying routes often results in very long dispatch routes, imprecise forest area coverage, and a high extra coverage rate. An intelligent fusion algorithm was proposed to address these problems. A vector modeling method (VMM) was proposed to determine the optimum spraying route with the minimum number of turnarounds and the lower extra coverage rate. An improved genetic algorithm (IGA) was used to plan the dispatch route. Subsequently, the improved SSA (ISSA) was utilized to plan the takeoff/landing site location. An artificial neural network (ANN)-based classifier was constructed to constrain the search domain of the ISSA to avoid ponds, lakes, and other areas unsuitable for takeoff/landing. The proposed intelligent fusion algorithm is called VMM-ISSA-IGA-ANN. The planning of the takeoff/landing site locations, dispatch routes, and spraying routes for helicopter plant protection operations was carried out in four forested environments in Zhengzhou, Kaifeng, Mingguang, and Jurong, China. The planning scheme was compared with that of an aviation company. The results showed that the takeoff/landing site location derived from the VMM-ISSA-IGA-ANN was more reasonable than that obtained by the aviation company. The dispatch route was shortened by 6.51%, 23.7%, 42.07%, and 49.80%, the spraying route was shortened by 1.94%, 0.22%, 0.05%, and 0.91%, the extra coverage rate was reduced by 29.15%, 24.92%, 14.46%, and 26.04%, and the number of turnarounds was reduced by 7, 2, 21, and 22 for the four locations, respectively. This study provides a theoretical basis for route planning of helicopter aerial spraying operations and a technical reference for smart forestry applications.

The Integrated Rescheduling Problem of Berth Allocation and Quay Crane Assignment with Uncertainty

The baseline plan of terminals will be impacted to a certain extent after being affected by uncertain events, such as vessel delay and unscheduled vessel arrival, resulting in disorderly terminal operations, wasted resources, and reduced loading and unloading efficiency, which further aggravates terminal congestion. To effectively cope with the disturbance of terminal operations by the above uncertain events and improve the operational efficiency of container terminals, this paper investigates the integrated rescheduling problem of berth allocation and quay crane assignment with vessel delay and unscheduled vessel arrival. Two steps are designed to deal with uncertainty shocks. The first step is to determine the rescheduling moment by using a rolling time-domain approach. The second step is to establish a rescheduling model and design an improved genetic algorithm(IGA) to obtain a rescheduling solution using various rescheduling strategies at the rescheduling moment. Moreover, through scenario experiments, comparisons with commercial solvers and other algorithms, it can be seen that the solution speed of IGA is better than that of commercial solvers and the average gap does not exceed 6%, which verifies the effectiveness and superiority of this algorithm.

Hybrid prediction method of blast furnace gas generation considering multi-operation modes in steel plants near the city

Blast furnace gas (BFG) generated in blast furnace (BF) ironmaking process can be converted into steam and electricity to provide energy for the nearby cities. Accurate prediction of BFG generation is crucial for assessing BFG surplus, which is the difficult problem faced by steel plants. A hybrid prediction method is proposed to solve it, which is named Radial Basis Function Neural Network (RBFNN) Based on Improved Genetic Algorithm (IGA) and Grey Relation Analysis (GRA). BF’s operation modes, including smooth, blown-down, blow-down to 0, blow-in, were classified to exclude the unrelated feature factors and set the adaptive prediction horizon. Then feature factors related to BFG generation are quantitatively analyzed by GRA in different operation mode, which affects the structure of RBFNN. And the key parameters of RBFNN is optimized by IGA. Proposed method is validated by a case study from a China’s steel plant. The results show the hybrid method has the obvious advantage with the MAPE of 3.31 %, 3.22 % and RMSE of 337.57 m3 in blow down, blown-in and smooth operation mode, respectively. This paper helps steel plants assess how much steam and electricity converted from BFG can be sent to its nearby cities, and contributes to energy resources utilization.

Prediction Model for the Chemical Futures Price Using Improved Genetic Algorithm Based Long Short-Term Memory

In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA–LSTM model were compared with those of other models. The results showed that the IGA–LSTM model could effectively capture the characteristics and trends of time-series changes. The results showed that the proposed model obtained the minimum values (MSE = 0.00107, RMSE = 0.03268, and MAPE = 0.0691) in the forecasting of futures prices for two types of chemical products, showing excellent forecasting performance.

Increasing the Accuracy of Soil Nutrient Prediction by Improving Genetic Algorithm Backpropagation Neural Networks

Soil nutrient prediction has been eliciting increasing attention in agricultural production. Backpropagation (BP) neural networks have demonstrated remarkable ability in many prediction scenarios. However, directly utilizing BP neural networks in soil nutrient prediction may not yield promising results due to the random assignment of initial weights and thresholds and the tendency to fall into local extreme points. In this study, a BP neural network model optimized by an improved genetic algorithm (IGA) was proposed to predict soil nutrient time series with high accuracy. First, the crossover and mutation operations of the genetic algorithm (GA) were improved. Next, the IGA was used to optimize the BP model. The symmetric nature of the model lies in its feedforward and feedback connections, i.e., the same weights must be used for the forward and backward passes. An empirical evaluation was performed using annual soil nutrient data from China. Soil pH, total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were selected as evaluation indicators. The prediction results of the IGA–BP, GA–BP, and BP neural network models were compared and analyzed. For the IGA–BP prediction model, the coefficient of determination for soil pH was 0.8, while those for total nitrogen, organic matter, fast-acting potassium, and effective phosphorus were all greater than 0.98, exhibiting a strong generalization ability. The root-mean-square errors of the IGA–BP prediction models were reduced to 50% of the BP models. The results indicated that the IGA–BP method can accurately predict soil nutrient content for future time series.

Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm.

This study discusses the application of fuzzy adaptive PID and improved genetic algorithm (IGA) in motion control and path optimization of autonomous underwater vehicle (AUV). The fuzzy adaptive PID method is selected because it is considered to be a strongly nonlinear and coupled system. First, this study creates the basic coordinate system of the AUV, and then analyzes the spatial force from the AUV to obtain the control model of the heading angle, climb angle, and depth. Next, the knowledge of fuzzy adaptive PID and IGA technology on AVU are investigated, then fuzzy adaptive PID controllers and path optimization are established, and experimental simulations are carried out to compare and analyze the simulation results. The research results show that controllers and IGA can be used for the motion control and path optimization of AUV. The advantages of fuzzy adaptive PID control are less overload, enhanced system stability, and more suitable for motion control and path optimization of AUV.