HTSA: A Hybrid Tree Seed Algorithm based on Differential Evolution with Dynamic Population Diversity Maintenance

Based on the problem of Tree Seed Algorithm (TSA), this paper proposes a Hybrid Tree Seed Algorithm (Hybrid-Tree Seed Algorithm, HTSA) based on Differential Evolution and Dynamic Population Diversity Maintenance. HTSA effectively overcomes the limitations of the original TSA by introducing key enhancement mechanisms. By combining Bernoulli mapping initialization, HTSA ensures that the population distribution is more balanced and efficient compared to the stochastic mapping of TSA. Moreover, the introduction of a positional update formula inspired by the Sparrow Search Algorithm in the seed updating enhances population diversity and convergence, especially in the late optimization stages. Integration with Differential Evolution (DE) further enhances global exploration capabilities. Together, these modifications address issues such as premature convergence and sensitivity to local optimal values in TSA.Comparative experiments showed that HTSA outperformed TSA,EST-TSA and other benchmark algorithms, showing significant advantages in optimization power, convergence rate and population diversity.