Research on construction of effective function sets for general aviation aircraft products based on ant colony algorithm

As an optimization algorithm for simulating ant foraging behavior, ant colony algorithm has the advantages of positive feedback, distributed computing and strong robustness, and can effectively deal with the combinatorial optimization problem of product functions. Based on this, this paper conducts in-depth research on the traditional ant colony algorithm and innovatively proposes an optimization strategy for constructing function sets of general aviation aircraft products. This strategy centers around the importance of user needs. It conducts data collection and processing based on web crawlers and the TextRank algorithm. By constructing a tight correlation matrix among product functions, it then employs the ant colony algorithm to intelligently screen and optimize the combination of numerous functions. Through simulation experiments with aerial photography drones, this method effectively reduces redundancy and conflicts in function selection, optimizes function combinations, and achieves a good balance among multiple objectives.