Route Planning of Geomagnetic Aided Navigation for Vehicle under Matching Suitability Constraints

2013 ◽  
Vol 454 ◽  
pp. 39-42
Author(s):  
Peng Wang ◽  
Xiao Ping Hu ◽  
Mei Ping Wu ◽  
Hua Mu ◽  
Hai Ping Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Planning Method ◽  
Experimental Results ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Movement Distance

In geomagnetic aided navigation (GAN), the vehicle is expected to traverse the areas with excellent matching suitability in order to obtain high matching precision. The route planning problem under matching suitability constraints is studied based on particle swarm optimization (PSO) algorithm in this article. Firstly, the PSO algorithm is briefly introduced and the expanding space of route nodes is determined with the maneuverability constraints of the vehicle. Then the minimum movement distance, the ability of avoiding threats and the proximity to suitable-matching areas are considered to construct the fitness function of PSO algorithm. Further the route planning method under matching suitability constraints is proposed. Experimental results show that the proposed method is effective, and the vehicle can successfully avoid the threats and can traverse the suitable-matching areas.

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.

scholarly journals A Novel Three-Dimensional Path Planning Method for Fixed-Wing UAV Using Improved Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Chen Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Planning Problem ◽  
Dimensional Problem ◽  
Complex Environment ◽  
Swarm Optimization ◽  
Path Planning Problem

This paper proposed an improved particle swarm optimization (PSO) algorithm to solve the three-dimensional problem of path planning for the fixed-wing unmanned aerial vehicle (UAV) in the complex environment. The improved PSO algorithm (called DCA ∗ PSO) based dynamic divide-and-conquer (DC) strategy and modified A ∗ algorithm is designed to reach higher precision for the optimal flight path. In the proposed method, the entire path is divided into multiple segments, and these segments are evolved in parallel by using DC strategy, which can convert the complex high-dimensional problem into several parallel low-dimensional problems. In addition, A ∗ algorithm is adopted to generated an optimal path from the particle swarm, which can avoid premature convergence and enhance global search ability. When DCA ∗ PSO is used to solve the large-scale path planning problem, an adaptive dynamic strategy of the segment selection is further developed to complete an effective variable grouping according to the cost. To verify the optimization performance of DCA ∗ PSO algorithm, the real terrain data is utilized to test the performance for the route planning. The experiment results show that the proposed DCA ∗ PSO algorithm can effectively obtain better optimization results in solving the path planning problem of UAV, and it takes on better optimization ability and stability. In addition, DCA ∗ PSO algorithm is proved to search a feasible route in the complex environment with a large number of the waypoints by the experiment.

Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy

2014 ◽  
Vol 496-500 ◽  
pp. 1895-1900
Author(s):  
Wen Wang ◽  
Wei Shen ◽  
Chao Long Ying ◽  
Xin Yi Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Search Strategy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Optimal Search ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Convergence Performance

In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.

scholarly journals Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm

2021 ◽  
Vol 9 (4) ◽  
pp. 357
Author(s):  
Wei Zhao ◽  
Yan Wang ◽  
Zhanshuo Zhang ◽  
Hongbo Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Route Planning ◽  
Planning Method ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ship Route

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

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.

Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

2018 ◽  
Vol 41 (4) ◽  
pp. 942-953 ◽  
Author(s):  
Weidong Zhou ◽  
Zejing Xing ◽  
Bai Wenbin ◽  
Deng Chengchen ◽  
Yaen Xie ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Autonomous Underwater Vehicles ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Underwater Vehicles ◽  
Local Optimum ◽  
Swarm Optimization

The mission route plays an essential role for the mission security and reliability of an unmanned system. This paper gives a route planning method for autonomous underwater vehicles (AUVs) based on the hybrid of particle swarm optimization (PSO) algorithm and radial basis function (RBF). In the improved PSO algorithm, metropolis criterion is used to prevent the improved PSO algorithm from falling into local optimum and RBF is used to smooth the path planned by PSO algorithm. Compared with classic PSO algorithm, the hybrid algorithm of PSO and RBF can avoid falling into the local optimum effectively and plan an anti-collision route. Moreover, based on the simulation results, it can be seen that the approach presented here is more efficient in convergence performance, and the planned route requires lower performance of AUVs.

scholarly journals Investigation on Photovoltaic Array Modeling and the MPPT Control Method under Partial Shading Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jianbo Bai ◽  
Leihou Sun ◽  
Rupendra Kumar Pachauri ◽  
Guangqing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Output Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Control Mode ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Pv Array ◽  
Pv Modules

On the basis of a five-parameter photovoltaic (PV) mathematical model, a multipeak output model of a PV array under partial shading conditions (PSCs) is obtained by MATLAB simulation. Simulation and experimental results demonstrate that the model can simulate the performance curves of the PV array under the PSCs. Optimized particle swarm optimization (OPSO) is used to control the multipeak output model that can quickly and accurately track the global maximum power point (GMPP) of PV modules under PSCs. Its main idea is to determine the initial position of particles and remove the acceleration factor and random number in traditional particle swarm optimization (PSO) algorithm. Additionally, according to the distance between two consecutive peak points, the maximum value of velocity is obtained. The advantages of the OPSO include the following: compared with the traditional PSO algorithm, the computing time is greatly shortened; and it is easy to achieve the MPPT with a low-cost microprocessor. In addition, a PV optimizer is designed to improve the output power of PV modules under PSCs, and simulation and experimentation have compared the output characteristics of PV modules in traditional control mode and optimized control mode under PSCs. The experimental results show that the PV optimizer improves the output power of the PV modules by 13.4% under the PSC.

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.

Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

2021 ◽  
pp. 014233122199883
Author(s):  
Zhen Tan ◽  
Hua-Geng Liang ◽  
Dan Zhang ◽  
Qing-Guo Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Planning Method ◽  
Transformation Model ◽  
Local Drug Delivery ◽  
Pathological Examination ◽  
Swarm Optimization

Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particle swarm optimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.

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