Task allocation of multiple vehicles to attack targets by using particle swarm optimization algorithm

2021 ◽  
Author(s):  
Ruping Zou ◽  
Jianshu Liu ◽  
Hongbo Shi ◽  
Yingmin Yi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multiple Vehicles

Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

2020 ◽  
pp. 194-209
Author(s):  
Arindam Majumder ◽  
Rajib Ghosh
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Multi Robot

This study deals with a plant layout where there were ninety predefined locations which have to be inspected by using three multiple robots in such a way that there would not be any collisions between the robots. A heuristic integrated multiobjective particle swarm optimization algorithm (HPSO) is developed for allocating tasks to each robot and planning of path while moving from one task location to another. For optimal path planning of each robot the research utilized A* algorithm. The task allocation for each robot is carried out using a modified multiobjective particle swarm optimization algorithm where the earliest completion time (ECT) inspired technique is used to make the algorithm applicable in multi robot task allocation problems. At the later stage of this study, in order to test the capability of HPSO an instance is solved by the algorithm and is compared with the existing solutions of a genetic algorithm with the A* algorithm. The computational results showed the superiority of the proposed algorithm over existing algorithms.

scholarly journals An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm

2018 ◽  
Vol 36 (4) ◽  
pp. 722-727
Author(s):  
Guoqing Shi ◽  
Fan Wu ◽  
Lin Zhang ◽  
Shuyang Zhang ◽  
Cao Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Allocation Method

The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.

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