Immune Genetic Algorithm Research Articles

Intelligent Height Adjustment Method of Shearer Drum Based on Rough Set Significance Reduction and Fuzzy Rough Radial Basis Function Neural Network

The intelligent adjustment method of the shearer drum is the key technology to improve the intelligent level and safety degree of fully mechanized mining face. This paper proposes a shearer drum intelligent height adjustment model based on rough set significance attribute reduction (AR) and fuzzy rough radial basis function neural network (FRRBFNN) optimized by adaptive immune genetic algorithm (AIGA). The model first selects the parameters of shearer process monitoring based on the importance attribute reduction algorithm of rough set, and establishes the attribute reduction set of shearer operation characteristic parameters and the drum height decision rule base. Next, a fuzzy rough radial basis function neural network determined by the decision rule space is proposed. By introducing the fuzzy rough membership function as the connection weight, the network can accurately describe the complex nonlinear relationship between the working characteristic parameters of the attribute shearer and the drum height, and measure the uncertainty of the coal seam distribution. Finally, to further optimize the performance of FRRBFNN, the adaptive immune genetic algorithm is introduced to optimize its parameters, to build a high-precision shearer drum height prediction system. For the evaluation method of the model, we use three indicators: mean absolute error, mean absolute percentage error, and root mean square error. Based on the measured data in Yujialiang area, Shaanxi Province, the experimental results show that—compared with the FRRBFNN and support vector regression (SVR) models, a gated current neural network (GRU), a radial basis function neural network (RBF), the memory strengthen long short-term memory (MSLSTM) model, and the adaptive fuzzy reasoning Petri net (AFRPN)—the MAE of the AR-AIGA-FRBFNN model for predicting the height of the left and right rollers are 18.3 mm and 17.2 mm, respectively; the MAPE is 0.96% and 0.93%, respectively; and the RMSE is 21.2 mm and 22.4 mm, respectively. The AR-AIGA-FRBFNN model is therefore more effective than the other considered methods.

Path planning algorithm of dual arm space intelligent robot

In order to solve the cooperation problem between the two arms of the dual arm robot, improve the effect and accuracy of the path planning of the intelligent robot, in this study, a new path planning algorithm for dual arm space (DAS) intelligent robot is designed. By analyzing the kinematics performance of DAS intelligent robot, the operation law of DAS intelligent robot is determined. By analyzing the collision that needs to be considered during the operation of DAS intelligent manipulator, a collision detection method based on capsule envelope is proposed to detect the collision of obstacles in the running path. Finally, the path planning model of DAS intelligent robot is designed by using immune genetic algorithm. The experimental results show that the path planned by this method can avoid obstacles and dangerous areas, and the accuracy of path planning for the existing environment is always higher than 90%. With the increase of iteration times, the planning time is relatively stable, about 5.5 seconds. The proposed method can enhance the effect and accuracy of path planning, effectively shorten the time of path planning, and improve the work efficiency of DAS intelligent robot.

Retracted: Research on Structural Optimization of Prefabricated Components Based on Improved Immune Genetic Algorithm.

[This retracts the article DOI: 10.1155/2022/4648112.].

Location Selection for Regional Logistics Center Based on Particle Swarm Optimization

The location of a logistics center is very important in a logistics system, as the success of the location determines the whole logistics system’s structure, shape, and mode, and not only affects the logistics center’s own operating costs, performance, and future development, but also affects the operation of the entire logistics system. Therefore, the selection of the location for a logistics center has great significance for improving the efficiency of regional logistics and optimizing the structure of a logistics system. This study constructed a multi-factor constrained P-median site-selection model to optimize the locations of logistics centers to improve the efficiency of logistics and optimize the structure of the logistics system in a region. The results show that the optimal distribution of logistics center sites and the coverage of freight capacity demand derived from the particle swarm algorithm are more balanced than those derived by the other algorithm. Following the comparison of the results for the utility of the optimized layout points solved by the particle swarm algorithm and the immune genetic algorithm, it is concluded that the optimal fitness value obtained by the particle swarm algorithm is lower than the other. It is proven that the particle swarm algorithm of the P-median site-selection model under this multi-factor constraint has some reference value for the selection of the sites of multi-logistics centers.

Optimizing site selection for construction demolition waste treatment plants considering demand and supply uncertainty: a case study in Chongqing, China

The reasonable location of treatment plants can improve the recovery rate of construction and demolition waste (C&DW). However, most studies ignore the impact of uncertainty or are based on known probability information. This study establishes an optimization site selection model using a robust method, which takes into account the uncertainty of the recycling amount of C&DW and the demand for recycled products. The immune genetic algorithm is applied to solve and analyse the model. Chongqing, the only municipality in south-west China, is taken as a case study. The results show that: (1) transportation costs affect the behaviour of construction contractors in recycling C&DW; (2) site selection schemes and total costs vary with changes in the disturbance ratio and uncertainty level, and the uncertainty of supply has a greater impact on the costs; and (3) when the uncertainty level and disturbance rate reach a certain value, the site selection scheme changes.

Research on detection mechanism of vessel abnormal behavior based on immune genetic spectral clustering algorithm

Maritime transport accounts for over 90% of global trade, and maritime safety has been confirmed as a vital issue of maritime transport. Accordingly, vessel traffic service system (VTS) is capable of assisting the maritime department to complete real-time supervision of a certain water area and increasing the efficiency of water supervision. However, the VTS system has low effectiveness in vessel anomaly detection and supervision, thus resulting in regulatory blind spot in the VTS system. In this study, a vessel trajectory anomaly detection mechanism is developed using the immune genetic spectral clustering method. Moreover, the contour coefficient of scatter clustering is accurately analyzed, and the clustering center is optimized using the adopted method. On that basis, different abnormal features can be detected, and abnormal detection accuracy can be increased. In this study, the vessel trajectory anomaly detection model is built in accordance with the set sliding window and abnormal threshold parameters of vessel trajectory characteristics. The AIS data of Lianyungang to Qingdao port from March 2021 are selected for experimental analysis. The results suggest that the proposed method outperforms the conventional method in the detection accuracy and the false alarm rate, it facilitates the intelligent and automatic management of vessels by the VTS system.

Research on Network Customer Classification Based on BP Neural Network Algorithm

Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. A new customer classification model for online trading customer classification is presented based on analyzing customer characteristics and behaviors .First the paper designs 21 customer classification indicators based on consumer characteristics and behaviors analysis, including customer characteristics type variables and customer behaviors type variables; Second, Aiming at the shortages of the existing BP neural network algorithm of data-mining for customer classification, immune genetic algorithm is used to correct BP neural network to speed up the convergence of the model. Finally the experimental results verify that the new algorithm can improve effectiveness and validity of customer classification when used for classifying network trading customers practically.

A method of regulating the wind field to reduce safety hazards of gas and dust in fully mechanized heading face based on dynamic data

Accumulation of gas and dust in excavation work is a safety risk that remains to be solved. A substantial amount of gas and dust accumulate in fully mechanized heading face outlets, and the press-in tube jet cannot be adjusted dynamically based on the actual physical conditions. To solve the problem, a dynamic data-driven method for optimising gas and dust distribution by regulating the wind field is proposed. This method is based on the immune genetic algorithm and uses dynamic data as the incremental data with the historical data from the mine regulation rules to obtain the optimal incremental regulation rules (IRRs) for the wind field. The experiment was performed in Ningtiaota coal mine, Yulin city, northern Shaanxi, China. The observed wind velocity and gas concentrations were within the specification range when regulated by IRRs. The dust concentrations at the position of the driver and the average concentrations on the return air side decreased by 64.6 % and 56.5 %, respectively, when the outlet was 5 m from the head-on. When the distance of the outlet from the head-on was 10 m, the dust concentrations at the position of the driver and the average concentrations on the return air side dropped by 42.9 % and 68.6 %, respectively. The results from this study provide measures that would improve safety and efficiency during excavation of fully mechanized heading faces.

Improved immune genetic algorithm based TEG system reconfiguration under non-uniform temperature distribution

To deal with the problem of low thermoelectric conversion efficiency in thermoelectric power generation (TEG), this work designs an improved immune genetic algorithm (IIGA) to reconfigure TEG system under non-uniform temperature distribution (NTD) conditions to maximize power output. The traditional IGA is a fusion algorithm that combines genetic algorithm (GA) with immune algorithm (IA). Due to IGA's flaws of strict parameter setting and long computation time, three mountain factors are updated dynamically in IIGA to enhance the balance between local exploitation and global exploration. Consequently, the electrical connection of the TEG system is dynamically adjusted and reconfigured via IIGA, which can effectively improve power generation efficiency and reduce power consumption. The effectiveness and reliability of IIGA are testified and compared against four well-established meta-heuristic algorithms under two cases, i.e., 9 × 9 small TEG system and 15 × 15 large TEG system. Simulation results indicate that the output power boost by IIGA reaches 7.502 % under the 9 × 9 small TEG system and 10.281 % under the 15 × 15 large TEG system. Furthermore, a hardware-in-the-loop (HIL) experiment based on RTLAB platform is undertaken to testify the hardware implementation feasibility of IIGA affected by NTD conditions.

Research on Communication Site Planning and Regional Clustering Based on Optimal Multivariate Optimization Model

In recent years, in the context of the continuous development of 5G networks, the construction of mobile communication mobile base stations is extremely important for the overall network environment construction in my country. The sites in the objective function are planned multiple times through the immune genetic algorithm, and finally the optimal site is re-planned and the weak coverage points are clustered.

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

To reduce the complexity and multiple constraints of logistics distribution routing problems, the author proposes an improved genetic algorithm, adaptive immune genetic algorithm (AIGA). The algorithm utilizes a new immune vaccine selection strategy and immune operation method, the optimization process is adaptively changed with the evolutionary algebra, combined with the parallel selection method to optimize the multiobjective logistics distribution path, and the specific steps to solve the multiobjective logistics distribution path problem are given. The experimental results show that the optimal path is calculated as 0—2—8—5—3—1—0; 0—4—7—6—0. AIGA not only efficiently converges to the optimal solution but also stabilizes to a fitness value of 66.5, reflecting better precision than IGA. The computational efficiency and convergence of the algorithm are significantly improved, which verifies the practicability and effectiveness of the algorithm. The adaptive immune genetic algorithm realizes the path optimization of complex logistics distribution, which can better change the global search performance of the original immune genetic algorithm, greatly improve the algorithm convergence speed, and achieve good results in practical applications.

Immune Optimization of Double-Sided Welding Sequence for Medium-Small Assemblies in Ships Based on Inherent Strain Method

The double-sided welding process is widely used in ship construction due to its high welding efficiency and forming quality. In order to further reduce the deformation caused by double-sided welding for medium-small assemblies in ships, the optimization of the double-sided welding sequence based on an artificial immune algorithm is carried out. First, the formation mechanism of welding deformation under the double-sided welding process is analyzed by the inherent strain method; next, the reduction of the welding deformation is taken as the optimization goal, and the welding sequence optimization model for double-sided welding is constructed; then, an immune clonal optimization algorithm based on similar antibody similarity screening and steady-state adjustment is introduced, and the immune optimization process for double-sided welding sequence is designed; finally, double-sided welding sequence optimization tests are carried out for four different types of medium-small assemblies in ships. Numerical test results show that, compared with IGA (Immune genetic algorithm), ICA (Immune clonal algorithm), and GA (Genetic algorithm), the maximum welding deformation caused by the welding sequence optimized by the proposed immune clonal algorithm is reduced by 2.4%, 2.8, and 3.3%, respectively, the average maximum welding deformation is reduced by 2.6%, 2.5, and 3.4%, respectively, and the convergence generation is reduced by 16.2%, 13.4, and 11.2%, respectively, which verifies the strong optimization ability and high optimization efficiency of the immune clonal algorithm introduced in the double-sided welding sequence optimization.

An Improved Immune Genetic Algorithm for Solving the Flexible Job Shop Scheduling Problem with Batch Processing

This paper presents a mathematical model for the flexible job shop scheduling problem (FJSP) with batch processing for manufacturing enterprises with both the flexible job shop scheduling problem and a batch process (BP) problem in actual production. An improved immune genetic algorithm (IGA) based on greedy thought combined with local scheduling rules is used to solve this scheduling problem. In the flexible job shop part, the greedy optimal solution is obtained through the greedy thought. The concept of cross-entropy is then introduced to improve the standard IGA. Calculating the cross-entropy of the individual and greedy optimal solutions for optimization considerably accelerates the optimization speed of the algorithm and enhances the ability of the algorithm to escape the local optimum. In the batching process, effective batching rules are designed to reduce blockage and improve batching efficiency; thus, the job can quickly and effectively pass the batching process and complete the entire production process. In the algorithm verification stage, standard FJSP datasets are used to simulate and verify the proposed algorithm. Considering the specific FJFP with BP problem, we perform simulation experiments with actual production data of a transformer manufacturer. The results show that the proposed method can effectively solve such problems.

A multi-cycle and multi-echelon location-routing problem for integrated reverse logistics

PurposeThe rapid development of smart cities and green logistics has stimulated a lot of research on reverse logistics, and the diversified data also provide the possibility of innovative research on location-routing problem (LRP) under reverse logistics. The purpose of this paper is to use panel data to assist in the study of multi-cycle and multi-echelon LRP in reverse logistics network (MCME-LRP-RLN), and thus reduce the cost of enterprise facility location.Design/methodology/approachFirst, a negative utility objective function is generated based on panel data and incorporated into a multi-cycle and multi-echelon location-routing model integrating reverse logistics. After that, an improved algorithm named particle swarm optimization-multi-objective immune genetic algorithm (PSO-MOIGA) is proposed to solve the model.FindingsThere is a paradox between the total cost of the enterprise and the negative social utility, which means that it costs a certain amount of money to reduce the negative social utility. Firms can first design an open-loop logistics system to reduce cost, and at the same time, reduce negative social utility by leasing facilities.Practical implicationsThis study provides firms with more flexible location-routing options by dividing them into multiple cycles, so they can choose the right option according to their development goals.Originality/valueThis research is a pioneering study of MCME-LRP-RLN problem and incorporates data analysis techniques into operations research modeling. Later, the PSO algorithm was incorporated into the crossover of MOIGA in order to solve the multi-objective large-scale problems, which improved the convergence speed and performance of the algorithm. Finally, the results of the study provide some valuable management recommendations for logistics planning.

Innovative Sensor Design Method of Industrial Ceramic Product Modeling Based on Immune Optimization Algorithm

A new method based on immune optimization algorithm was proposed for modeling and design of industrial ceramic products. Based on the joint coding of fuzzy rules and membership function parameters, an improved immune optimization algorithm was used to realize the synchronous optimization of industrial ceramic product modeling. A superheated steam temperature control system is taken as an example to carry out simulation tests. The results show that the ratio of the unit volume to the side length is between 0 and 1, which is called the mass of the grid element. Zero is the worst quality and one is the best. Element aspect ratio and distortion degree are also basic inspection indexes. The average mass of vacuum glass is 0.86, the aspect ratio is 1.71, and the element distortion degree is 0.13, indicating that the grid quality is well divided. It is proved that using the improved immune genetic algorithm to optimize fuzzy control can overcome the defects of the traditional genetic algorithm and ensure the faster and more stable search for the optimal rule base and membership function.

Research on Structural Optimization of Prefabricated Components Based on Improved Immune Genetic Algorithm.

On the premise of satisfying the design conditions and practical engineering needs, it is of practical significance to optimize the design of assembly components. Therefore, in this paper, based on the improved immune genetic algorithm, the structural optimization of prefabricated components is deeply studied, where it is converted into binary gene code, and the structural design requirements are introduced into the algorithm through structural layout vaccine and concrete strength vaccine. In addition, the section size of components is designed according to economic indicators, which has proved that the research is effective for the structural optimization method of prefabricated components.

Dynamic Evaluation and Prediction for Macroeconomic Based on IGA-LSSVM

Among all statistics, government statistics plays an extremely important role in socioeconomic activities, while the continuous changes of external factors bring new challenges to government statistics and have a great impact on the quality of statistics. With the rapid development of the economy and the maturity of the market economy system, the demand for government statistics from all walks of life has become more and more vigorous. In order to effectively improve the accuracy of macroeconomic outflow forecasting and further secure economic production, a weighted least squares support vector machine (LS-SVM) optimized with the immune genetic algorithm (IGA) is proposed to build a macroeconomic outflow forecasting model. For the nonlinearity, time-varying, and complexity of the economic outflow system, a new weighted strategy function is proposed to improve the LS-SVM, and then the IGA is introduced to optimize the improved LS-SVM with the kernel parameter δ and the regularization parameter γ. Finally, an experimental analysis is conducted using economic emergence data from economic history. The results show that the maximum relative error of forecasting using the model is 2.763%, the minimum relative error is 0.705%, the average relative error is 1.3298%, and the model has faster convergence, stronger generalization ability, and higher forecasting accuracy than other forecasting models.

Concrete Vehicle Scheduling Based on Immune Genetic Algorithm

At present, the demand for ready-mixed concrete (RMC) in construction industry is increasing day by day, and the supply mode of multiple delivery depots corresponding to multiple construction sites has been widely used. In order to further improve the joint distribution efficiency between various delivery depots, this research establishes a multiobjective optimal distribution model with time window constraints and demand postponement attributes for the problem that the subbatching plants need to work together. The model divides the reasons for demand postponement into two types: the constraint for timely unloading of trucks cannot be met on time and the constraint for timely pouring at the construction site cannot be met on time. This work improved the coding method of genetic algorithm based on the characteristics of the distribution model. Using hierarchical real-coding form, the coding operator of each layer can be evolved separately, which ensures the globality of the search, and, at the same time, an improved immune operator is added to ensure the local search performance. By comparison, the results obtained by improved GA are 7.05% higher than those of the standard GA, and the early convergence speed of improved GA is obviously better than that of the standard GA. The simulation experiments show that the total trucks’ waiting time during the process of providing delivery services from 5 concrete plants to 8 construction sites is 769 minutes, and the total waiting time of 8 construction sites is 507 minutes. Through practical case analysis, this work can enable RMC production enterprises and construction sites to effectively reduce the waiting time of corresponding operations, and the obtained results are close to the simulation results. The proposed method indeed improves the efficiency of RMC distribution.

Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm.

In order to make key decisions more conveniently according to the massive data information obtained, a spatial data mining technology based on a genetic algorithm is proposed, which is combined with the k-means algorithm. The immune principle and adaptive genetic algorithm are introduced to optimize the traditional genetic algorithm, and the K-means, GK, and IGK algorithms are compared and analyzed. The results show that, in two different datasets, the objective functions obtained by the K-means algorithm are 94.05822 and 4.10373 (×106), respectively, while the objective functions obtained by the GK and IGK algorithms are 89.8619 and 3.9088 (×106), respectively. The difference between the three algorithms can also be reflected in the data comparison of the number of iterations. The number of iterations required for k-means to reach the optimal solution is 8.21 and 8.4, respectively, which is the most among the three algorithms, while the number of iterations required for IGK to reach the optimal solution is 5.84 and 4.9, respectively, which is the least. Although the time required for K-means is short, by comparison, the IGK algorithm we use can get the optimal solution in relatively less time.

Design of an Intelligent Nursing Information Management System for Critically Ill Patients in Neurosurgery.

In hospitals, one of the dominant issues is the development of an accurate and precise nursing management system which is hard to implement due to the various problems in the implementation of the traditional manual system. For this purpose, we are to solve the imperfect functions of the traditional nursing information management system and the strong subjectivity and low accuracy of the way of manually judging the patient's condition. Firstly, the Immune Genetic Algorithm (IGA) is used to optimize the Backpropagation Neural Network (BPNN). A mortality prediction model using IGA-BPNN is proposed. Secondly, a nursing information management system for critically ill patients in neurosurgery is designed. The IGA-BPNN prediction model is used as a part of the system to predict the mortality of critically ill patients. Finally, the performance of the predictive model and the system is tested using the Medical Information Mart for Intensive Care (MIMIC)-III data set design experiment. The results show the following: (1) Precision, Recall, and F1-score of mortality prediction using the IGA-BPNN model are 7.2%, 7.2%, and 7.3% higher than those of other prediction models. The designed model has better performance. (2) The comprehensive performance of the system during operation can reach the standard. The researched content aims to provide important technical support for the nursing information management of critically ill patients in neurosurgery and the intelligent analysis of patients' condition.