scholarly journals Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Aerospace ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Shuangxi Liu ◽  
Fengping Huang ◽  
Binbin Yan ◽  
Tong Zhang ◽  
Ruifan Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Index System ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Adaptive Pso

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

Nonlinear Inertia Weigh Particle Swarm Optimization Combines Simulated Annealing Algorithm and Application in Function and SVM Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3467-3471 ◽  
Author(s):  
Bin Jiao ◽  
Zhi Xiang Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Local Equilibrium ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Performance ◽  
Support Vector ◽  
Swarm Optimization ◽  
Searching Ability

This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing (SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.

A Modified Particle Swarm Optimization Algorithm with Cases Studies

2011 ◽  
Vol 268-270 ◽  
pp. 823-828
Author(s):  
Cheng Chien Kuo ◽  
Hung Cheng Chen ◽  
Teng Fa Taso ◽  
Chin Ming Chiang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Fast Calculation ◽  
Modified Particle Swarm Optimization ◽  
Stochastic Optimization Algorithms

s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made from different algorithms are then compared between the quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the simulation results, SA-PSO obtained higher efficiency, better quality and faster convergence speed than other compared algorithms.

scholarly journals Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (8) ◽  
pp. 3417
Author(s):  
Nafis Ahmed ◽  
Chaitali J. Pawase ◽  
KyungHi Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Human Beings ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Area Surveillance

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

scholarly journals Determining of geometrical characteristic parameters of particle fractal aggregates from light scattering measurement signals

2020 ◽  
pp. 203-203
Author(s):  
Liang Xu ◽  
Zhen-Zong He ◽  
Jun-Kui Mao ◽  
Xing-Si Han
Keyword(s):  
Particle Swarm Optimization ◽  
Light Scattering ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fractal Aggregates ◽  
Swarm Optimization ◽  
Repulsive Particle Swarm Optimization ◽  
Light Scattering Measurement ◽  
Scattering Measurement

Two kind of light scattering measurement methods, i.e. the forward light scattering measurement (FLSM) method and the angular light scattering measurement (ALSM) method, are applied to reconstruct the geometrical morphology of particle fractal aggregates. An improved Attractive and Repulsive Particle Swarm Optimization (IARPSO) algorithm is applied to reconstruct the geometrical structure of fractal aggregates. It has been confirmed to show better convergence properties than the original Particle Swarm Optimization (PSO) algorithm and the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm. Compared with the FLSM method, the ASLM method can obtain more accurate and robust results as the distribution of the fitness function value obtained by the ALSM method is more satisfactory. Meanwhile, the retrieval accuracy can be improved by increasing the number of measurement angles or the interval between adjacent measurement angles even when the random noises are added. All the conclusions have important guiding significance for the further study of the geometry reconstruction experiment of fractal aggregates.

scholarly journals An Improved Simulated Annealing Particle Swarm Optimization Algorithm for Path Planning of Mobile Robots Using Mutation Particles

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jianzhang Lu ◽  
Zhihao Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Path Planning ◽  
Mobile Robots ◽  
Human Life ◽  
Optimal Path ◽  
Particle Swarm ◽  
Autonomous Driving ◽  
Pso Algorithm ◽  
Swarm Optimization

Artificial intelligence technology has brought tremendous changes to human life and production methods. Mobile robots, UAVs, and autonomous driving technology have gradually entered people’s daily life. As a typical issue for a mobile robot, the planning of an optimal mobile path is very important, especially in the military and emergency rescue. In order to ensure the efficiency of operation and the accuracy of the path, it is crucial for the robot to find the optimal path quickly and accurately. This paper discusses a new method and MP-SAPSO algorithm for addressing the issue of path planning based on the PSO algorithm by combining particle swarm optimization (PSO) algorithm with the simulated annealing (SA) algorithm and mutation particle and adjusting the parameters. The MP-SAPSO algorithm improves the accuracy of path planning and the efficiency of robot operation. The experiment also demonstrates that the MP-SAPSO algorithm can be used to effectively address path planning issue of mobile robots.

Structure Optimal Design for Portable Exoskeleton Using Improved Particle Swarm Optimization

2012 ◽  
Vol 204-208 ◽  
pp. 4845-4850
Author(s):  
Fang Liu ◽  
Wen Ming Cheng ◽  
Yi Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimal Design ◽  
Particle Swarm ◽  
Lower Extremities ◽  
Legged Locomotion ◽  
Design Parameters ◽  
Position Vector ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Portable exoskeleton is directed at providing necessary support and help for loaded legged locomotion. The kernel of whole mechanical construction of the exoskeleton is lower extremities. The lower extremities consist of exoskeleton thigh, exoskeleton shank, hydraulic cylinder and corresponding joints. In order to find the optimal combination of design parameters of lower extremities, an improved particle swarm optimization algorithm based on simulated annealing is proposed. To improve global and local search ability of the proposed approach, the inertia weight is varied over time, and jumping probability of simulated annealing is adopted in updating the position vector of particles. Experimental results show that the improved algorithm can obtain the optimal design solutions stably and effectively with less iteration compared to the standard particle swarm optimization and simulated annealing; using ANSYS software build finite element model with the optimization result, then analyzes the strength of the model, these stress results verify the of accuracy of the improved particle swarm optimization.

Optimization of PMSM Sensorless Control Based on Improved PSO Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 86-92
Author(s):  
Miao Wang Qian ◽  
Guo Jun Tan ◽  
Ning Ning Li ◽  
Zhong Xiang Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Sensorless Control ◽  
Fitness Function ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Extend Kalman Filter ◽  
Swarm Optimization ◽  
Manual Adjustment

For the problem that manual adjustment of the parameters of controller in sensorless control system costs too much time, manpower and always can not get a good result, a new method based on improved particle swarm optimization algorithm is proposed to optimize the parameters. The improved algorithm is based on the standard particle swarm optimization with the simulated annealing algorithm and chaotic search brought in. The speed of motor is estimated by the extend Kalman filter. The error between measured speed and estimated speed of the permanent magnet synchronous motor rotor is used as the fitness function in order that the parameters in the covariance matrix is adjusted.The result of simulation indicates that high estimation precision can be got and the motor represents steadily with few of ripple of the actual speed.With this method, the time of adjustment is reduced and manpower is saved. In addition, the validity of the method is proved in experiment with dSPACE.

Optimal Design of Lower Extremity for Portable Human Exoskeletons Using Improved Particle Swarm Optimization

2012 ◽  
Vol 538-541 ◽  
pp. 3215-3221
Author(s):  
Fang Liu ◽  
Wen Ming Cheng ◽  
Nan Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimal Design ◽  
Particle Swarm ◽  
Lower Extremities ◽  
Legged Locomotion ◽  
Design Parameters ◽  
Position Vector ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Portable powered human exoskeleton is directed at providing necessary support and help for loaded legged locomotion. The kernel of whole mechanical construction of the exoskeleton is lower extremities. The lower extremities consist of exoskeleton thigh, exoskeleton shank, hydraulic cylinder and corresponding joints. In order to find the optimal combination of design parameters of lower extremities, an improved particle swarm optimization algorithm based on simulated annealing is proposed. To improve global and local search ability of the proposed approach, the inertia weight is varied over time, and jumping probability of simulated annealing is adopted in updating the position vector of particles. Experimental results show that the improved algorithm can obtain the optimal design solutions stably and effectively with less iteration compared to the standard particle swarm optimization and simulated annealing.

Design on Filter Parameters with Hybrid PSO Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1888-1891
Author(s):  
Yi Zhang ◽  
Ke Wen Xia ◽  
Gen Gu
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Filter Model ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Model Setting

In order to solve the problems in the optimization of filter parameters, such as large amounts of calculation and the complicated mathematical hypotheses, an approach to optimize filter parameters is presented based on the Hybrid Particle swarm optimization (HPSO) algorithm, which includes the establishing of filter model, setting up the fitness-function and optimizing filter parameters by HPSO algorithm. The application example shows that the optimization method improves the design accuracy and saves calculation, and HPSO algorithm is superior to PSO algorithm in optimization of filter parameters.

Research on AC Asynchronous Motor Vector Control Speed Control System Based on Labview

2015 ◽  
Vol 9 (1) ◽  
pp. 547-552
Author(s):  
Song Ren-Yin ◽  
Liang Guo-Xiang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Simulation Analysis ◽  
Asynchronous Motor ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Electric Parameters

During the process of using traditional particle swarm optimization (PSO) to identify electric parameters, the algorithm can be easily caught in locally optimal solution, thus leading to relatively large identification result error. Therefore, the article proposes a simulated annealing particle swarm optimization (SA-PSO) algorithm to integrate the advantages, namely the strong global optimization capability of simulated annealing (SA) algorithm and the fast convergence speed of PSO algorithm, in order to improve the traditional PSO algorithm, and uses simulated annealing principle to determine inertia weight of PSO algorithm. Meanwhile, DFIG with the unit capacity of 1.5MW has been taken as the research object for simulation analysis. The test system employs LabVIEW software for experiment design, and the result shows: compared with the identification result of traditional PSO algorithm, SA-PSO algorithm can rapidly and accurately identify DFIG electric parameters and the identification result has higher precision.

Sign in / Sign up

Export Citation Format

Share Document