Improved Multi-Strategy Fusion Whale Optimization Algorithm Based on Sobol Sequence and Fermat Point Traction

The Whale Optimization Algorithm (WOA) is widely used in optimization problems due to its simple structure, but it suffers from slow convergence, low accuracy, and a tendency to fall into local optima. To address these issues, an improved multi-strategy fusion algorithm (SFIMWOA) based on the Sobol sequence and Fermat point traction strategy is proposed. First, the richness of the initial population is improved using the Sobol quasi-random sequence. Second, the position information of the triple-optimal solutions in the population is utilized to generate Fermat points, and directed traction is applied to overbounded individuals to enhance population utilization. Finally, variable adaptive rate factors are introduced to dynamically balance the global and local searches. To verify the effectiveness of the proposed algorithm, comparative simulation tests between SFIMWOA and six other optimization algorithms are conducted using the CEC2017 test suite and real engineering problems. The results show that SFIMWOA exhibits faster convergence speed, higher accuracy, and greater robustness.