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.

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition

2018 ◽  
Vol 32 (06) ◽  
pp. 1859008 ◽  
Author(s):  
Chen Huang ◽  
Jiyou Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Competition Strategy ◽  
Swarm Optimization ◽  
Evaluation Functions ◽  
Planning Methods

Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.

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.

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.

scholarly journals UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yudong Zhang ◽  
Lenan Wu ◽  
Shuihua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Minimum Probability ◽  
Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

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.

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