An AD-SPSO Algorithm for Solving the Measurement Resource Scheduling Problem with Satellite Cataloging Requirements

Under the condition of limited ground measurement resources, the increasing scale of satellites brings more satellite cataloging requirements and aggravates the measured pressure. How to better utilize limited measurement resources is a crucial issue. In this paper, the measurement resource scheduling problem with satellite cataloging requirements is examined, a measurement resource scheduling model is established with the goal of maximizing profits from satellite cataloging task completion, and a switching particle swarm optimization algorithm based on adaptive large neighborhood search and dynamic neighborhood (AD-SPSO) is proposed. The AD-SPSO algorithm uses the time greedy strategy and chaos mapping to obtain an initial population with better quality. Then, the algorithm designs the dynamic neighborhood of the particle, after which the particle's velocity is updated by selecting the optimal position from the dynamic neighborhood of the particle, so as to enhance the inter-particle information interaction and utilize the population information. In addition, the AD-SPSO algorithm joins the adaptive large neighborhood search algorithm, which selects different particles for perturbation according to the population state. This effectively prevents its early convergence. To evaluate the performance of the AD-SPSO algorithm, this paper performs experiments in four scenarios, and in different scenarios, compared with other improved algorithms, the AD-SPSO algorithm can obtain higher and more stable overall profit for scheduling plans, which verifies its effectiveness in solving the measurement resource scheduling problem with satellite cataloging requirements.