Multi-Strategy Collaborative Improved Seagull Optimization Algorithm and Its Applications

In this paper, in order to improve the search ability and adaptability of the seagull optimization algorithm, a multi-strategy collaborative improvement-based seagull optimization algorithm (MI-SOA) is proposed. Firstly, LogisticsTent chaotic mapping with is introduced to enhance the global search ability of the algorithm; secondly, the algorithm's search path is optimized by filtering out the superior and inferior solution locations through the inverse learning strategy to improve the quality of the solution; the algorithm combines the Lévy flights and the positive cosine operator, which enhances the algorithm's local search accuracy and flexibility; the improved seagull optimization algorithm has been developed in the mathematical model and parameter settings are optimized, and by adjusting the iterative process and parameter selection of the algorithm, the algorithm can be better adapted to various complex optimization problems. The experimental results show that the MI-SOA algorithm has better performance and faster optimization ability than other algorithms in solving numerical optimization problems; the improved seagull optimization algorithm is applied to three-dimensional path planning problems in practical engineering applications.