Immune Particle Swarm Optimization Research Articles

Strip Steel Defect Prediction Based on Improved Immune Particle Swarm Optimisation–Improved Synthetic Minority Oversampling Technique–Stacking

Strip Steel Defect Prediction Based on Improved Immune Particle Swarm Optimisation–Improved Synthetic Minority Oversampling Technique–Stacking

Optimization of Berth-Tug Co-Scheduling in Container Terminals under Dual-Carbon Contexts

In order to address the dynamic changes in vessel preferences for berth lines caused by the deployment of shore-based power equipment in major ports and the collaborative scheduling problem of berthing and towing assistance, this paper quantifies the environmental costs of pollutants from the main engines of tugs and auxiliary engines of container ships using an environmental tax. Additionally, considering the economic costs such as vessel delay and shore power cable connection, a two-layer mixed-integer linear programming model is constructed using the task sequence mapping method. This model integrates the allocation of continuous berths at container terminals with coordinated towing scheduling for shore power selection. A solution approach is designed by combining the commercial solver (CPLEX) and the immune particle swarm optimization algorithm (IAPSO). The proposed scheme is validated using the example of the Nansha Phase IV Terminal at the Port of Guangzhou. The results show that compared to the traditional first-come-first-served and adjacent scheduling schemes, the collaborative scheduling scheme proposed in this paper reduces the total cost by 21.73%. By effectively utilizing berth resources and shore power equipment while densely arranging collaborative tasks and appropriately increasing the number of tugs, the port can convert the economic cost of leasing a small number of tugs (increased by 10.63%) into environmental benefits (decreased by 33.88%). This approach provides a reference for addressing nearshore pollution emissions in ports.

Two-level optimal scheduling of source–storage-load interactive distribution network based on particle swarm optimization algorithm

In response to the difficulties of grid integration and consumption of a high proportion of new energy generation, as well as the high pressure on traditional thermal power generation to regulate peak loads, this paper constructs a wind-solar-fire-water complementary power generation system with “dual energy storage” and proposes a two-layer scheduling optimization strategy for this system. The scheduling model’s higher layer uses the system’s complementary power generation system’s maximum benefit as its objective function. The lower tier model has the minimization of the system’s cost of regulating load peaks as the objective function, and the model is solved by the improved adaptive immune particle swarm optimization algorithm considering various constraints. Taking the improved IEEE30 node system as an example for analysis, according to the operation of the original power system and the different characteristics of pumped storage and storage power plants, pumped storage and energy storage power stations are gradually added to the system for scheduling comparative analysis, and different capacities are configured, respectively. Through the analysis of examples, it is verified that the proposed dual-storage joint scheduling optimization strategy can effectively improve the overall operation of the system, reduce the peaking pressure of thermal power units, and achieve the expected goal of maximizing economic benefits. It also improves the new energy consumption in the region and alleviates the thermal power peaking pressure.

Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm

In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path. To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest. Especially in the complex level 4 wave water environment, the AIPSO algorithm had the best adaptability and robustness, and the robot had the highest working efficiency and comprehensive performance. The experimental results revealed that the AIPSO algorithm effectively improved the path search and garbage cleaning efficiency of the robots and reduced the working time, which further verified the reliability and accuracy of the designed algorithm.

Software-hardware embedded system reliability modeling with failure dependency and masked data

Traditional system reliability models often ignore failure dependency between subsystems and the existence of system failure masked data, thereby they can’t accurately reflect the reliability modeling analysis of the whole system. This paper investigates an embedded system that comprises software subsystems and hardware subsystems. In order to more accurately assess the embedded system’s reliability, we proposed a reliability superposition model of the software-hardware system with masked data and failure dependency. In the model, the influence of masked failure data and failure dependency between the hardware subsystem and software subsystem is considered in system reliability evaluation, and the influence of fault dependence is solved by Copula function. Estimating the parameters of this model is a challenging task due to the complexity of the parameters. With regards to this, we proposed an immune particle swarm optimization algorithm with enhanced learning ability (IPSO-ELA), which is used to calculate the parameter’s estimation. Additionally, we investigated the impact of varying degrees of failure dependence on system reliability. Finally, the numerical experiment shown that the proposed model, which considers failure dependency among subsystems, outperforms other reliability models that do not. It can be seen from the experimental fitting results and the reliability trend charts that the system reliability when failure dependence is considered is higher than isn’t considered, which is more realistic.

Application of Improved Immune Particle Swarm Optimization in 3D Node Localization

When 2D localization algorithm is extended to 3D space, it achieves node localization with better localization accuracy. An improved immune particle swarm optimization (IIPSO) localization algorithm is proposed to estimate the localization of nodes in wireless sensor networks (WSN). The results show that IIPSO algorithm outperforms the traditional two-dimensional localization algorithm based on iterative estimation of the center of mass by 3%-6%. The proposed algorithm has better resistance to RSSI measurement errors and can achieve more than 99% node localization coverage through multiple iterations, which is effective for WSN.

Fuzzy optimization of radon reduction by ventilation system in uranium mine

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

A Strategy of Multi-edge Collaborated Task Offloading Based on Edge Servers Placement in Metropolitan Area Networks

At present, multi-access edge computing is applied to “cloud-edge-terminal” collaborative computing, in which the task of computing offloading is used to unload the computing tasks of “terminal” to “edge”, i. e. edge servers, but the placement of edge servers is often not considered, resulting in low efficiency of computing offloading. Therefore, this paper proposes a multi-edge collaborative task unloading strategy for the effective placement of edge servers in 5G metropolitan area networks (MAN). First, the strategy weights the average user delay, the average edge server load and the average edge server resource utilization, and improves the firefly algorithm (FA) to optimize the number and effective location of edge servers. Secondly, a multi-edge collaborative task unloading architecture is presented. The computing tasks in this architecture can be executed locally, on a local server, on a remote server or in the “cloud”. The delay and energy consumption of the four unloading modes are modeled respectively. Thirdly, a new variable, i. e. the maximum cooperation cost that the “terminal” can bear, is introduced to attract more remote edge servers to cooperate to complete the calculation of the “terminal” task. Furthermore, the immune particle swarm optimization (IPSO) algorithm was designed to solve the problems of the traditional PSO algorithm. The simulation results show that the improved firefly algorithm can find the optimal coordinates of the edge server, and then carry out the task unloading of the multi-edge server. Compared with the local offload strategy, immune algorithm (IA) and PSO, the total cost of the proposed IPSO algorithm is reduced by 66.7%, 54% and 45.5%, respectively. Therefore, the algorithm in this paper can improve the execution efficiency of computing tasks and effectively reduce the total cost of the whole system.

Disclosure Model of Capital Accounting Information Based on Immune Particle Swarm Optimization Algorithm

Disclosure Model of Capital Accounting Information Based on Immune Particle Swarm Optimization Algorithm

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles (EVs) continues to grow and the demand for charging infrastructure is also increasing, how to improve the charging infrastructure has become a bottleneck restricting the development of EVs. In other words, reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system. Considering the construction and maintenance of the charging station, the distribution network loss of the charging station, and the economic loss on the user side of the EV, this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function, and thus proposes a location and capacity planning model for the EV charging station. Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm, the simulated annealing immune particle swarm optimization algorithm (SA-IPSO) is adopted in this paper. The simulated annealing algorithm is used in the global update of the particle swarm optimization (PSO), and the immune mechanism is introduced to participate in the iterative update of the particles, so as to improve the speed and efficiency of PSO. Voronoi diagram is used to divide service area of the charging station, and a joint solution process of Voronoi diagram and SA-IPSO is proposed. By example analysis, the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost, while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%. The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1% and basically does not fall into local convergence.

Microgrid operation measurement evaluation method based on blockchain technology

As the power market reform continues to deepen, microgrid plays an increasingly important role in promoting the local consumption of new energy and environmental protection. Therefore, it is of great significance to study the power market transaction with the participation of multiple microgrids.Blockchain technology is being used in many industries due to its decentralized and reliable data.This paper analyzes the advantages of the application of block chain in the micro-grid market, combines the advantages of block chain with the micro-grid market, and proposes a transaction architecture of the micro-grid market based on block chain technology.At the same time, this paper proposes a two-level optimization method.Finally, in this paper, through the simulation analysis method in promoting micro network market transactions, to improve the validity of the participants income by using the immune particle swarm optimization (PSO) algorithm to solve the model analysis.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

A K-Means Clustering Algorithm for Early Warning of Financial Risks in Agricultural Industry

As the primary industry, agricultural industry is the basis of guaranteeing people’s basic life and national economic development. Agricultural industrial finance/financial is a weak link in the financial system, which seriously hinders the emergence of agricultural scale effect and the improvement in agricultural production efficiency. In order to find the financial risk of agricultural industry in time, this article proposes an agricultural industry financial risk early warning system based on improved K-means clustering algorithm. Because the traditional K-means algorithm is easy to fall into local optimization in the clustering process, the clustering effect is not reliable. In this article, the idea of immune cloning and particle swarm optimization location update is added to the grey wolf optimization algorithm. Grey wolf optimization algorithm and K-means algorithm are combined to solve the problem that K-means algorithm is easy to fall into local optimization. In the experimental part, through comparative verification, it can be found that the prediction performance of this system is superior to other models. Its practical application value is higher. Therefore, choosing this system for early warning of agricultural industry finance can effectively improve the accuracy of early warning and provide guarantee for the economic development of agricultural industry.

IPSMT: Multi-objective optimization of multipath transmission strategy based on improved immune particle swarm algorithm in wireless sensor networks

Multipath transmission routing in dynamic wireless sensor network deployed with movable nodes is a multi-objective optimization issue with multi-parameter constraints. Traditional strategies have the deficiencies such as high computational complexity, long solving time, difficulty in obtaining optimal value and easy to fall into local solution especially in the scenario of large quantity of movable sensor nodes being deployed. Therefore, an improved immune particle swarm optimization based multipath transmission strategy (IPSMT) is presented. It contains three parts: improved immune particle swarm algorithm (IIPSO), IPSMT and fault tolerance strategy in multipath transmission routing (FTMT). IIPSO aims to improve the diversity of particle population and accelerate the convergence of multipath establishment respectively through concentration regulation mechanism and immune vaccine operation improvement. IPSMT and MTFT is to establish and optimize multiple transmission paths by considering the movable nodes’ moving distance and energy consumption quality by multi-objective optimization method. Network fault tolerance model has been established to analyze the algorithm’s convergence, energy consumption balance, network fault tolerance and time complexity. Through the multi-objective optimization simulation and analysis of multipath establishment comparing with the related works, IPSMT shows good global searching ability and convergence performance as well as diversity of solution population to realize the optimization of multipath transmission routing. The whole network is proved to have good performance of transmission stability and fault tolerance.

Research on gait recognition and prediction based on optimized machine learning algorithm

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

A cooperative game model of supply chain logistics information based on collaborative immune quantum particle swarm optimisation

A cooperative game model of supply chain logistics information based on collaborative immune quantum particle swarm optimisation

A cooperative game model of supply chain logistics information based on collaborative immune quantum particle swarm optimisation

A cooperative game model of supply chain logistics information based on collaborative immune quantum particle swarm optimisation

Disclosure Model of Capital Accounting Information Based on Immune Particle Swarm Optimization Algorithm

The effectiveness of capital market and the allocation of social resources depend on the disclosure of capital accounting information. In order to analyze the tendency of capital accounting information disclosure, this paper proposes a disclosure model of capital accounting information based on immune particle swarm algorithm. There are many factors that affect the tendency of capital accounting information disclosure. We should give priority to corporate governance level and financial status level to construct the impact index system of capital accounting information disclosure. The capital accounting information disclosure model was constructed to establish the functional relationship between each factor variable and disclosure tendency. Particle concentration was maintained through immune memory and self-regulation mechanism to ensure the diversity of the population, which avoids the traditional shortcomings of particle swarm optimization algorithm. Finally, the parameter estimation of capital accounting information disclosure model were completed. The results show that there are four factors affecting the disclosure tendency of capital accounting information, including ownership structure, leverage, growth and audit opinion. The accuracy of the model used in this paper is up to 75%.

Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network

The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other industrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the rationality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k-means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimization algorithm work in stages to complete the training of the consequent part parameters. The proposed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling.

LED based high accuracy indoor visible light positioning algorithm

In light of the problem of low precision and time consuming in indoor three-dimensional positioning of visible light communication (VLC), we propose an indoor three-dimensional positioning system of VLC based on improved immune particle swarm optimization (IIMPSO) algorithm. To reduce the influence of reflection, we select the optimal field of view by analyzing the indoor multipath effect, and improve the positioning model in the state of inclination. Moreover, we use Kalman filter algorithm to decrease the influence of environmental interference on the received power. Then, we apply IIMPSO algorithm to the indoor VLC three-dimensional positioning system to improve the positioning precision and convergence speed. The simulation results indicate that the average positioning error is 3.12 cm, and the convergence time is 2.03 s. Compared with existing positioning systems, the new proposed three-dimensional positioning system are significantly improved in the positioning precision and convergence speed.