WTSA: A Whale-enhanced Tree-Seed Algorithm with Timed Iterative Update Mechanism

This paper presents an improved Tree Seed Optimization Algorithm, namely the Whale Optimization Algorithm with Adaptive Search Strategy (WTSA), designed to address the limitations of traditional Tree Seed Optimization Algorithms. WTSA incorporates an advanced adaptive search mechanism and a dynamic adjustment framework to enhance the balance between exploration and exploitation, overcoming issues such as premature convergence and entrapment in local optima. The algorithm adopts an Iterative Chaos Initialization strategy, combining deterministic and stochastic methods to achieve a more uniform and diversified initial population distribution. Additionally, WTSA introduces a dynamic search step-size adjustment, significantly improving convergence speed and solution quality. The integration of multi-strategy search methods enables WTSA to effectively navigate complex search spaces and identify global optima. Through extensive comparative experiments with advanced algorithms, including the original Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and TSA variants, the results demonstrate WTSA’s superior performance in solution accuracy and computational efficiency. The findings highlight WTSA’s robustness and versatility in solving various optimization problems, making it a competitive choice for complex real-world applications.