Immune Particle Swarm Optimization Algorithm Research Articles

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.

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.

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.

Tool path planning for flank milling of non-developable ruled surface based on immune particle swarm optimization algorithm

Aiming at the tool position optimization problems in flank milling of non-developable ruled surface with conical tools, a new method based on immune particle swarm optimization (IPSO) and least squares (LS) is proposed. The geometric features of the non-developable ruled surface are analyzed and the error measurement function under each tool position is used as the objective function of the intelligent algorithm. It means that the sum of the distances from the discrete points on the axis of the conical tool to the design surface is the minimum by adjusting the cutter axis and the offset distance to the design surface. The initial tool position is determined by two-point offset method. On this basis, the objective function is optimized by the immune particle swarm optimization algorithm to obtain the optimized tool position. Since the geometric decomposition method is adopted, the optimization is essentially a local optimization, so the least squares method is further used for global optimization, and the overall optimized tool position is obtained. The simulation results show that the machining error is reduced by 86% and the validity of the method is verified by machining experiments finally.

Optimum Short Circuit Current Limiter Deployment Using Immune-PSO Algorithm

The optimum of short circuit current limiter deployment (SCCLD) is a 0-1 mixed integer programming problem. It is non-continuous, non-convex and nonlinear. Ordinary mathematic optimization can hardly solve it. In this paper, the sensitivity factor was defined to choose better candidates for active current-limiting measure installations as to reduce bus fault currents. And the integrated measures deployment is solved by a discrete particle swarm optimization algorithm with immune (IA-DPSO). The results of IEEE 39-bus system in New England are provided to validate the possible applications of the proposed method.

Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions

Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation.

An Intelligent Algorithm for Solving the Efficient Nash Equilibrium of a Single-Leader Multi-Follower Game

This aim of this paper is to provide the immune particle swarm optimization (IPSO) algorithm for solving the single-leader–multi-follower game (SLMFG). Through cooperating with the particle swarm optimization (PSO) algorithm and an immune memory mechanism, the IPSO algorithm is designed. Furthermore, we define the efficient Nash equilibrium from the perspective of mathematical economics, which maximizes social welfare and further refines the number of Nash equilibria. In the end, numerical experiments show that the IPSO algorithm has fast convergence speed and high effectiveness.

Indoor Three-dimensional Positioning System Based on Visible Light Communication Using Improved Immune PSO Algorithm

For the problem that the three-dimensional positioning accuracy is not high and the positioning time is too long in indoor Visible Light Communication(VLC). An indoor visible light three-dimensional positioning system based on Improved IMmune Particle Swarm Optimization(IIMPSO) algorithm is proposed. By analyzing the indoor multipath effects, the fitter Field Of View (FOV) is selected to reduce the influence of the reflection. Meanwhile, the positioning model under the tilt state is improved. The Kalman filter algorithm is used to reduce the impact of environmental interference on the received power. On the basis, it is integrated with the improved immune particle swarm algorithm. Simulation results show the average positioning error of the indoor three-dimensional positioning system is 0.031 m, and the positioning time is 2.3 s in the indoor of 5 m × 5 m × 3 m. Compared with the existing three-dimensional positioning system, the positioning accuracy and convergence speed are significantly improved.

On Condition Maintenance Model for Complex Electromechanical Equipments Based on Remaining Useful Life and Wiener Process

Aiming at the problem of condition based maintenance modeling for complex electromechanical equipment, a condition based maintenance model based on residual life prediction and improved Wiener process is proposed; first of all, for the degradation process character of complex electromechanical equipment, this paper proposes a residual life prediction model based on random coefficient Wiener process; To estimate the parameters of degradation process, a prior distribution construction method based on state data is proposed; And the posterior distribution and estimation of degradation process parameters are constructed by Bayes theory, and the remaining life prediction of equipment in operation is realized. Secondly, the decision model of condition based maintenance is established by balancing the reliability and economy targets with the information of remaining life; And the immune particle swarm optimization algorithm is used to solve the model. The case study shows that the residual life prediction model proposed in this paper has higher prediction accuracy and lower prediction uncertainty, the condition based maintenance decision-making model is more practical, has higher operational reliability and economy, and the convergence accuracy of the model solving algorithm is higher.

A New Algorithm to Solve the Generalized Nash Equilibrium Problem

We try a new algorithm to solve the generalized Nash equilibrium problem (GNEP) in the paper. First, the GNEP is turned into the nonlinear complementarity problem by using the Karush–Kuhn–Tucker (KKT) condition. Then, the nonlinear complementarity problem is converted into the nonlinear equation problem by using the complementarity function method. For the nonlinear equation equilibrium problem, we design a coevolutionary immune quantum particle swarm optimization algorithm (CIQPSO) by involving the immune memory function and the antibody density inhibition mechanism into the quantum particle swarm optimization algorithm. Therefore, this algorithm has not only the properties of the immune particle swarm optimization algorithm, but also improves the abilities of iterative optimization and convergence speed. With the probability density selection and quantum uncertainty principle, the convergence of the CIQPSO algorithm is analyzed. Finally, some numerical experiment results indicate that the CIQPSO algorithm is superior to the immune particle swarm algorithm, the Newton method for normalized equilibrium, or the quasivariational inequalities penalty method. Furthermore, this algorithm also has faster convergence and better off-line performance.

A Multi-Objective Permanent Basic Farmland Delineation Model Based on Hybrid Particle Swarm Optimization

The delimitation of permanent basic farmland is essentially a multi-objective optimization problem. The traditional demarcation methods cannot simultaneously take into account the requirements of cultivated land quality and the spatial layout of permanent basic farmland, and it cannot balance the relationship between agriculture and urban development. This paper proposed a multi-objective permanent basic farmland delimitation model based on an immune particle swarm optimization algorithm. The general rules for delineating the permanent basic farmland were defined in the model, and the delineation goals and constraints have been formally expressed. The model introduced the immune system concepts to complement the existing theory. This paper describes the coding and initialization methods for the algorithm, particle position and speed update mechanism, and fitness function design. We selected Xun County, Henan Province, as the research area and set up control experiments that aligned with the different targets and compared the performance of the three models of particle swarm optimization (PSO), artificial immune algorithm (AIA), and the improved AIA-PSO in solving multi-objective problems. The experiments proved the feasibility of the model. It avoided the adverse effects of subjective factors and promoted the scientific rationality of the results of permanent basic farmland delineation.