Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm
As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is pro...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/11/12/1030 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850050347290066944 |
|---|---|
| author | Anqi Xu Hui Li Yun Hong Guoji Liu |
| author_facet | Anqi Xu Hui Li Yun Hong Guoji Liu |
| author_sort | Anqi Xu |
| collection | DOAJ |
| description | As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements. |
| format | Article |
| id | doaj-art-662cd129ec7f4ddaaaf09a8b29ee21c3 |
| institution | DOAJ |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-662cd129ec7f4ddaaaf09a8b29ee21c32025-08-20T02:53:30ZengMDPI AGAerospace2226-43102024-12-011112103010.3390/aerospace11121030Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization AlgorithmAnqi Xu0Hui Li1Yun Hong2Guoji Liu3School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaAs the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements.https://www.mdpi.com/2226-4310/11/12/1030particle swarm optimizationcollaborative decision-makingalgorithm optimization |
| spellingShingle | Anqi Xu Hui Li Yun Hong Guoji Liu Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm Aerospace particle swarm optimization collaborative decision-making algorithm optimization |
| title | Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm |
| title_full | Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm |
| title_fullStr | Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm |
| title_short | Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm |
| title_sort | autonomous decision making for air gaming based on position weight based particle swarm optimization algorithm |
| topic | particle swarm optimization collaborative decision-making algorithm optimization |
| url | https://www.mdpi.com/2226-4310/11/12/1030 |
| work_keys_str_mv | AT anqixu autonomousdecisionmakingforairgamingbasedonpositionweightbasedparticleswarmoptimizationalgorithm AT huili autonomousdecisionmakingforairgamingbasedonpositionweightbasedparticleswarmoptimizationalgorithm AT yunhong autonomousdecisionmakingforairgamingbasedonpositionweightbasedparticleswarmoptimizationalgorithm AT guojiliu autonomousdecisionmakingforairgamingbasedonpositionweightbasedparticleswarmoptimizationalgorithm |