Research on UAV Conflict Resolution Algorithm Based on Improved Particle Swam Algorithm

Civil aviation transportation equipment is more convenient and faster than other transportation tools and is an essential part of intelligent transportation. It is significant to study the reliability of positioning information and enhance traffic safety. Advanced receiver autonomous integrity monitoring (ARAIM) can provide vertical guidance during the different navigation stages in civil aviation fields. The traditional multiple hypothesis solution separation (MHSS) algorithm distributes the probability of hazardous misleading information (PHMI) and probability of false alarm (PFA) uniformly over all visible satellites resulting in reduced global availability of ARAIM. Aiming at this problem, we proposed an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm to redistribute integrity and continuity risks and establish a protection level optimization model. Based on the real BeiDou navigation satellite system/global positioning system (BDS/GPS) data, the experimental results show that the optimized algorithm can reduce the vertical protection level (VPL), and the ARAIM global availability of BDS/GPS is improved by 1.73%∼2.73%. The optimized algorithm can improve the availability of integrity monitoring at different stages of the navigation system and provide a basis for ensuring the reliability of the positioning results.

The five axis linkage Computer Numerical Control machine tool for integral impeller can achieve blade machining through side milling, which is of great significance for improving the machining accuracy, production efficiency, and long-term stability of integral impeller blades. This study is based on non-uniform rational B-spline curves and aims to reduce the surface over cutting or under cutting of integral turbine blades. The path planning of non deployable ruled surfaces was analyzed in depth through side milling, and the path planning model of the side milling cutter axis was solved through a fusion algorithm of simulated annealing algorithm and particle swarm optimization algorithm, in order to find the optimal path through iterative process. As the number of iterations increased, the error values of particle swarm optimization algorithm and simulated annealing particle swarm optimization fusion algorithm gradually decreased, with convergence times of about 7 and 6, respectively. The stable error value of the fusion algorithm was 0.253, which is 30.45% lower than that of the particle swarm optimization algorithm. The optimal number of iterations for solving the model using particle swarm optimization algorithm and fusion algorithm was the 7th, with range values of 0.0213 and 0.0165 mm, respectively. The tool axis trajectory surface optimized by the fusion algorithm was closer to the tool axis motion state compared to the initial tool axis trajectory surface. The range of the sum of mean squared deviations for single and global cutting was 0.0011–0.0198 and 0.046–0.0341, but the overall error value was relatively small. This study effectively reduces the envelope error of machining tools and improves machining accuracy, thereby solving the principle error of non expandable ruled surfaces in the motion trajectory of the blade axis of the integral turbine. This provides new research ideas for the intelligent development of Computer Numerical Control machining technology.

High-precision wireless localization has attractive application prospects. Cooperative localization is an effective tool to improve localization accuracy. However, compared with non-cooperative localization, in cooperative localization networks, large-scale neighboring links and nonlinear measurement functions cause the associated objective function to be non-convex. It is difficult to obtain global optimum using classical particle swarm optimization (PSO) algorithm or analytical methods. In order to solve this problem, a classified particle swarm optimization (CPSO) algorithm is proposed in this paper. For classical PSO, all search particles have the same inertial weight and learning factor. Unlike classical PSO, the proposed CPSO algorithm classified different search particles based on particle cost value and set different inertial weights and learning factors for search particles. Meanwhile, considering the unavoidable reference node location error, localization result could be achieved by calculating the weighted average of close-range particle locations. Simulation results prove that the CPSO algorithm improves positioning accuracy by 25.3% compared with classical PSO algorithm.

In view of the problem of premature convergence of traditional particle swarm optimization (PSO) algorithm in path planning, which is easy to converge to local optimal solution and poor path quality, some theory about the corresponding PSO algorithm of simulated annealing optimization is studied in this paper. While planning the moving path of the robot, it analyzes the effect of initial temperature and cooling coefficient on path length and iteration times from the major contributing factors of simulated annealing algorithm. Thus deduce the law of its change and seek the optimal parameter matching. Simulated annealing algorithm can not only move the updated particle position on the basis of the particle swarm optimization formula, but also select the updated position with a certain probability. The method is used to avoid the particle converging into the local optimal solution in the whole iterative process. The capabilities of the global optimization is strengthened. Compared with the traditional PSO algorithm, the simulated annealing PSO in complex environment has better optimization ability, shorter path and fewer iterations in the simulation results.KeywordsParticle swarmPath planningSimulated annealingLinear inertia weight

As an important transportation hub connecting the two sides of the Yellow River, the Yellow River Grand Bridge is of great significance for strengthening regional exchanges and promoting the high-quality development of the Yellow River Basin. However, due to the complex terrain, changeable climate, high sediment concentration, long construction duration, complicated process, strong dynamic, and many factors affecting construction. It often brings many problems, including low quality, waste of resources, and environmental pollution, which makes it difficult to achieve the balance of multiple objectives at the same time. Therefore, it is very important to carry out multi-objective optimization research on the construction of the Yellow River Grand Bridge. This paper takes the Yellow River Grand Bridge on a highway as the research object and combines the concept of “green construction” and the national policy of “carbon neutrality and carbon peaking” to construct six major construction projects, including construction time, cost, quality, environment, resources, and carbon emission. Then, according to the multi-attribute utility theory, the objectives of different attributes are normalized, and the multi-objective equilibrium optimization model of construction time-cost-quality-environment-resource-carbon emission of the Yellow River Grand Bridge is obtained; finally, in order to avoid the shortcomings of a single algorithm, the particle swarm optimization algorithm and the simulated annealing algorithm are combined to obtain the simulated annealing particle swarm optimization (SA-PSO) algorithm. The multi-objective equilibrium optimization model of the construction of the Yellow River Grand Bridge is solved. The optimization result is 108 days earlier than the construction period specified in the contract, which is 9.612 million yuan less than the maximum cost, 6.3% higher than the minimum quality level, 11.1% lower than the maximum environmental pollution level, 4.8% higher than the minimum resource-saving level, and 3.36 million tons lower than the maximum carbon emission level. It fully illustrates the effectiveness of the SA-PSO algorithm for solving multi-objective problems.

The continuous increase of green energy supply on the power generation side gives rise to green power consumption challenges, and the rapid growth of emerging loads on the consumer side makes a large amount of adjustable load resources to be tapped. A green consumption pattern that coordinates production and consumption contributes to sustainable development. Considering the two-way interaction between sources and loads, the article designs a credit points response mechanism for green and friendly electricity consumption by users. The proposed credit points mechanism aims to increase the green power usage on the power generation side, and at the same time, explore the adjustable load on the user side to guide the users to use electricity in a friendly way. Based on the credit points response mechanism, a user green-friendly power consumption regulation model is constructed with the objective of maximizing the points. A park microgrid is selected as the research object, and the simulated annealing particle swarm optimization algorithm is used to solve the changes in the user load after the implementation of the credit points mechanism. The research results show that after implementing the green-friendly electricity consumption credit mechanism, the photovoltaic consumption in the park increased from 80,125.21 kW to 87,345.69 kW, and the photovoltaic consumption rate increased by 6.92%. Through optimized scheduling, users have obtained 50,082.57 green electricity points and 5923.68 demand response points. After considering the role of energy storage, the photovoltaic consumption capacity was further increased to 95,854.69 kW, the photovoltaic consumption rate reached 91.69%, and the total points increased to 61,650.62. This mechanism effectively smooths out peak loads and promotes efficient consumption of green electricity. The findings validate the rationality and effectiveness of the proposed credit points mechanism, which has certain reference value for green power consumption certification and user friendly power usage.

A migration method for service function chain based on failure prediction

Faulty or inaccurate sensors can lead to increased energy consumption, disrupt control systems, and potentially cause damage to equipment. To tackle this challenge, we propose the Circle Mapping and Adaptive t-Distribution Runge-Kutta optimizer (CTRUN) to optimize the hyperparameters of a Long Short-Term Memory (LSTM) neural network designed explicitly for chiller sensor fault detection. CTRUN incorporates circle chaotic mapping for initialization and utilizes adaptive t-Distribution mutation. We compared CTRUN's performance against several optimization algorithms, including the Runge-Kutta optimizer (RUN), particle swarm optimization (PSO), sparrow search algorithm (SSA), simulated annealing particle swarm optimization algorithm (SimuAPSO), adaptive spiral flying sparrow search algorithm (ASFSSA), secretary bird optimization algorithm (SBOA) and hippopotamus optimization (HO) algorithm. The results indicate that CTRUN significantly improves both convergence speed and optimization accuracy. Furthermore, we employ the Pearson correlation coefficient to assess feature correlations, allowing us to eliminate weakly correlated variables and boost diagnostic precision. The CTRUN-LSTM achieved an average improvement in fault detection rates of 13.89, 26.94, 33.17, and 12.05% for the tested sensors.

In this paper, the inverted pendulum is controlled by Quanser universal benchtop equipment-direct current (QUBE DC) motor, and the motor inverted pendulum system is identified when the sinusoidal signal is input. However, there is a large error when the angle of the pendulum is pre-identified by numerical algorithms for subspace state space system identification algorithm. Therefore, this paper designs a multi strategy optimization simulated annealing particle swarm optimization algorithm, which can accurately identify the complex system with sinusoidal input signal. In the identification experiments of sinusoidal signals with multiple frequencies and amplitudes, this paper found that the algorithm performs relatively best at a frequency of 8 rad/s. Moreover, at a frequency of 8 rad/s, the algorithm can quickly reduce the error to 1.69% within 100 generations. Finally, based on the hardware model identified by particle swarm optimization, this paper designs an explicit model predictive control method to control the inverted pendulum, and tests the constraint processing and anti-disturbance performance of the system under the voltage pulse interference with different duty cycles, which realizes the inverted pendulum balance and has robustness under the interference.

The traditional filter design based on Least Mean Square (LMS) algorithm which depends on step heavily, good performance cannot be achieved in some models. To solve this problem, mathematical model based on the searching for optimization coefficients of the filter is constructed, three improved algorithms based on the basic Particle Swarm Optimization (PSO) algorithm including Simulated Annealing PSO (SAPSO), Chaotic Local Search PSO (CLSPSO) and Chaotic Simulated Annealing PSO (CSAPSO) are applied to this problem. The simulation result shows that the CSAPSO algorithm can obtain optimization values efficiently and has better performance of convergence and effectiveness.

This paper introduces the relative principium of K-Means algorithm, simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm at first. Then, in allusion to the influence of the initial value of the K-Means algorithm on the optimal solution of the algorithm, a hybrid algorithm of K-Means based on SA-PSO is proposed. The new algorithm uses the advantage of jumping out of local minima to improve the performance of the PSO algorithm, with the global optimization, the new algorithm can overcome the shortcoming of the K-Means algorithm which is easy to fall into the local optimal solution. The experimental result shows that the K-Means algorithm based on the hybrid SA-PSO algorithm compared with the PSO algorithm based on the K-Means algorithm has been partially effective increase in Global convergence.

Active disturbance rejection control (ADRC) has been widely used in the permanent magnet linear synchronous motor (PMLSM) drive system for its strong anti‐disturbance ability and fast response speed. However, a large number of adjustment parameters add difficulties to its application. To solve this problem, a novel sliding mode‐based active disturbance rejection control (SM‐ADRC) with autotuned parameters is proposed in this paper. In this control strategy, the SM is incorporated with extended state observer (ESO) and nonlinear state error feedback (NLSEF) to reduce the number of adjustment parameters. The simulated annealing particle swarm optimization (SAPSO) algorithm is carried out to achieve the optimal configuration and realize parameter self‐tuning. The experimental results of the PMLSM speed servo system demonstrate the proposed control method has better suppression of chattering effect, disturbance rejection ability and fast dynamic response. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

One of the important issues of microgrid development is how to optimize the operation of the microgrid that contains different kinds of distributed generation. This paper presents the application of particle swarm algorithm, based on chaotic simulated annealing to optimize the operation of the microgrid. Considering the operation cost, environmental benefits and other performance indicators as the objective function, to establish the mathematical model of microgrid optimal operation, propose the method of microgrid optimal operation based on chaotic simulated annealing particle swarm algorithm, and applied it to solve the multi-objective and multi-constrained problems of microgrid optimal operation. The example analysis proved the effectiveness and feasibility of this algorithm.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability