Improved Seagull Optimization Algorithm Based on Multi-Strategy Integration

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Aiming at the defects of seagull optimization algorithm (SOA) in solving optimization problems, such as local optimization, slow convergence speed and low optimization accuracy, a seagull optimization algorithm (SCSOA) based on integration of improved Sobol sequence and Cauchy variation is proposed. First, initialize the population using the Sobol sequence to make the seagulls more evenly distributed in the initial solution space; Secondly, use a nonlinear function to replace the original convergence factor to guide the seagull to always maintain a larger body degree of freedom during the search process, enhance the global search ability, and avoid falling into the local optimum; Then, the Cauchy variation strategy is introduced, so that the individual can better find the optimal solution and enhance the ability of the algorithm to jump out of the local optimum; Finally, use the benchmark function to test the improved algorithm, and compare it with the original algorithm and the experimental results of other algorithms. The results show that SCSOA performs better in convergence speed and optimization accuracy, and the global optimization capability is also improved.