## 1 Introduction
designed a particle swarm optimization method for the resource-constrained project scheduling problem, aiming to minimize project duration. Using a parallel algorithm, they represented priorities through particles and transformed them into feasible schedules based on priority and resource constraints. Experimental results demonstrated that this method outperforms traditional algorithms [12]. [PERSON] et al. proposed several metaheuristic-based algorithms and introduced task clustering into the Earth Observation Satellite scheduling problem [13]. [PERSON] et al. combined the EOS problem with data transmission issues and designed a dual-population artificial bee colony algorithm to solve it [14]. Figure 1: Comparison of non-agile and agile satellites. [PERSON] and [PERSON] proposed priority scheduling methods and genetic algorithms for multi-orbit agile satellite scheduling [15]. However, most studies have ignored transition times or represented transition times as constants. [PERSON] et al. initially proposed a time-dependent agile satellite scheduling model and developed an adaptive large neighborhood search algorithm for this purpose [16]. Building upon this work, [PERSON] et al. augmented the ALNS algorithm with various operators and tabu strategies to address the AEOS problem [17; 18]. Additionally, they extended this method to multi-satellite scenarios by employing heuristic techniques to design a task allocation layer. The time-dependent AEOS was modeled as a multi-objective optimization problem and solved by a memetic algorithm. Machine learning algorithms, such as artificial neural networks (ANN), were utilized to solve satellite scheduling problems. [PERSON] et al. integrated genetic algorithms with neural networks, developing a data-driven genetic algorithm. They used historical data to train an ANN neural network for generating superior initial populations [19]. The time-dependent property was mathematically proved using the minimum transition time instead of the actual transition time. An adaptive parallel local search algorithm combines multiple algorithms to run in parallel and adds an adaptive process to the algorithms and operators; finally, data mining techniques were utilized to enhance understanding quality and reduce computation time. Despite extensive research on AEOS scheduling, there is a lack of literature addressing time-dependent agile satellite scheduling. Furthermore, given the complex task requirements and high real-time demands currently faced in agile satellite scheduling, existing solutions, although partially addressing the scheduling problem, still suffer from low computational efficiency and unstable solution outcomes. Learning and optimization are symmetrical processes of solving problems. Based on problem knowledge, efficient algorithms could be designed to solve optimization problems. Thus, to address these issues, we propose a knowledge-guided parallel hybrid local search algorithm (KG-PHLS) algorithm to improve solution efficiency and benefits under larger-scale and more constrained conditions. The remaining manuscript is structured as follows. Section 2 primarily describes the model of the agile satellite scheduling problem and its relevant assumptions, while Section 3 provides a detailed explanation of each component of the knowledge-guided parallel hybrid local search algorithm. Section 4 presents the experimental data and environment, conducts algorithm testing, and analyzes the experimental results. The conclusion of the article is given in Section 5.