Agile Earth Observation Satellite Mission Planning Based on an Improved Brain Storm Optimization in Objective Space Algorithm

Traditional intelligent algorithms lack efficient utilization of knowledge of the agile earth observation satellite (AEOS) problem and the knowledge generated during the search process. To use the relevant knowledge and improve the algorithm-solving efficiency, this paper proposes an improved brain storm optimization in objective space (IBSOOS) algorithm based on knowledge strategy: 1) We use crossover and mutation strategies in the genetic algorithm (GA) to replace the original new solution generation strategy. This approach effectively handles non-continuous problems. 2) We introduce the worst solution updating strategy to enhance the algorithm's stability. During each iteration, this strategy adjusts the worst solution to learn from the target optimal solution. 3) We design a method for generating initial solutions, utilizing prior knowledge of the problem to pinpoint the range of candidate solutions swiftly. The experimental study compared the proposed algorithm with three other algorithms across four datasets. The results show that it significantly outperforms the others in solving AEOS mission planning problems.