Abstract
Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient transfer trajectory. This fuel efficiency maximization can then serve as the performance index of the OSV allocation optimization model problem. In particular, the maximization of the minimum residual fuel over individual OSVs is proposed as a performance index for OSV allocation optimization. The optimization problem is numerically solved through the proposed DPSO-TS algorithm. Finally, the simulation results demonstrate that the DPSO-TS algorithm has a higher accuracy compared to the DPSO, the DPSO-PDM and the DPSO-CSA algorithms in the premise that these four algorithms have the basically same computational time. The DPSO-TS algorithm can effectively solve the OSV allocation optimization problem.
Highlights
As humans explore space deeper, more and more diverse satellites are deployed into space
Many important problems of the Onorbit service vehicles (OSV) are investigated in the existing literature. e study of transfer trajectory is the basic work of allocation of OSVs
Multiple satellites need to be maintained in one service mission. e allocation of OSVs to satellites is an important problem that has the characteristics of a general resource allocation problem
Summary
As humans explore space deeper, more and more diverse satellites are deployed into space. Xin et al [14] propose an improved DPSO algorithm by designing the linearly decreasing inertia weight, which effectively solves the problem of scheduling MIMO radar tasks. E methods mentioned above improve the performance of DPSO algorithm by adjusting the inertia weight. Vairam et al [17] propose CHIDPSO algorithm by combining DPSO algorithm and cyber swarm algorithm, which improves the local search capacity of DPSO algorithm. In order to keep the balance of exploration and exploitation abilities, we introduce TS algorithm into DPSO algorithm to improve the performance of DPSO algorithm. E proposed method keeps the balance of exploration and exploitation abilities by controlling the inertia weight and the length of the tabu table. In order to make the paper readable, the nomenclature of variables is shown in Table 1 before the optimization models are established
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