Multi-strategy Collaborative Improvement of the Seagull Optimization Algorithm

To address issues such as low precision, slow convergence, and a tendency to fall into local optima in the seagull optimization algorithm, a Multi-Strategy Collaborative Improvement Seagull Optimization Algorithm (MSSOA) is proposed. This algorithm enhances performance through several strategies. Initially, the population is initialized using a combination of logistic and cubic chaotic maps to improve randomness. Nonlinear operations are then applied to control factors, transforming their linear variation into nonlinear variation. Additionally, Brownian motion is introduced to apply random disturbances to the seagull's position updates. Finally, the Metropolis criterion from simulated annealing is utilized to select and eliminate suboptimal seagull positions, thereby enhancing global search capabilities and mitigating the risk of local optima. Comparative studies with the basic seagull optimization algorithm, along with optimization performance evaluations on 23 types of test functions, demonstrate that MSSOA offers improved optimization accuracy and stability.