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

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.

2016 ◽  
Vol 11 (1) ◽  
pp. 58-67 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.


Author(s):  
ERIC A. RINCÓN-GARCÍA ◽  
MIGUEL A. GUTIÉRREZ-ANDRADE ◽  
SERGIO G. DE-LOS-COBOS-SILVA ◽  
PEDRO LARA-VELÁZQUEZ ◽  
ROMAN A. MORA-GUTIÉRREZ ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document