On-Orbit Servicing Task Allocation for Multi-Spacecrafts Using HDPSO

2014 ◽  
Vol 538 ◽  
pp. 150-153
Author(s):  
Ying Zhang ◽  
Qiang Zhang
Keyword(s):  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Allocation Problem ◽  
Multiple Constraints ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Simulation Results ◽  
Key Indicators

By analyzing the key indicators which contain the value of the target spacecrafts, the attrition of servicing spacecrafts and consumption of time and fuel, a mathematical model is formulated. And a hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed. Simulation results show that the algorithm can efficiently solve the multi-spacecrafts task allocation problem under multiple constraints.

Multi-Task Coalition Generation of Multi-Agent System with Limited Resource

2014 ◽  
Vol 971-973 ◽  
pp. 1655-1658
Author(s):  
Ning Qiang ◽  
Feng Ju Kang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Limited Resource ◽  
Multi Agent System ◽  
Discrete Particle Swarm Optimization ◽  
Agent System ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Agent ◽  
Simulation Results

A new fitness function is introduced in order to maximize the number of task served by the multi-agent system (MAS) with limited resource, while the tasks information remains unknown until the system found them one by one. The new fitness function not only considers to maximize the profit of the system which can be seen as to maximize the remaining resource of the system in the case of the MAS with limited resource, but also takes the balance of remaining resource in to account and it can makes a compromise between them. This paper uses an improved discrete particle swarm optimization to optimize the coalition of MAS. In order to improve the performance of the algorithm we redefine the particle velocity and position update formula. The simulation results show the effectiveness and superiority of the proposed fitness function and optimization algorithm.

Optimization of multi-task nodes in wireless sensor networks based on discrete particle swarm algorithm

2021 ◽  
pp. 1-12
Author(s):  
Xiuying Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Target Tracking ◽  
Task Allocation ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Target Tracking

Aiming at the competition conflict problem of task allocation of sensor node in wireless sensor network multi-target tracking, a discrete particle swarm optimization tracking task allocation optimization algorithm based on nearest neighbor is proposed. By constructing the mathematical model and objective function of the multi-objective multi-sensor node alliance cooperative tracking task allocation problem, the nearest neighbor method is used to initialize the particle group node task allocation, the objective function is used as the fitness function to guide the particle flight, and the optimal node allocation can be quickly realized. Experiments show that in the case of sparse node coverage, the particle swarm optimization node task allocation method has greatly reduced energy consumption compared with the nearest neighbor method, and can effectively solve the problem of multi-target tracking node task allocation conflict and multiple monitoring alliances on sensor resources the problem of increased system energy consumption during competition conflicts. Discrete particle swarm optimization has superiority for wireless sensor network multi-target tracking in actual environment.

On-Orbit Servicing Task Allocation for Spacecrafts Using Discrete Particle Swarm Optimization Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 574-580
Author(s):  
Qi Xin Zhang ◽  
Fu Chun Sun ◽  
Wen Ye ◽  
Jie Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Critical Index ◽  
Discrete Particle Swarm Optimization ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Update Strategy

The on-orbit servicing task allocation is very important to improve the cooperative work ratio of the on-orbit servicing spacecraft. A discrete particle swarm optimization (DPSO) algorithm is put forward for on-orbit servicing spacecraft cooperative task allocation problems. A new code of particles and new update strategy for the position and speed of particles are applied. By analyzing the critical index factors which contain target spacecraft value, servicing spacecraft attrition and energy-time consumption, on-orbit spacecraft task allocation model is formulated. The simulation results show that the DPSO algorithm has fast convergence, optimization capability, and can solve the on-orbit servicing spacecraft cooperative task allocation effectively.

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