Improved Tyrannosaurus Optimization Algorithm with Mixed Strategies

This paper addresses the problems of easy local optimum trapping, low convergence precision and slow convergence speed in the basic Tyrannosaurus (T-Rex) optimization algorithm (TROA) by proposing an improved Tyrannosaurus (T-Rex) optimization algorithm (ITROA). By using the logistic chaotic mapping method, ITROA enhances the population's quality and variety while improving convergence precision and global optimization performance. In order to optimize the Tyrannosaurus Rex hunting method, it simultaneously incorporates the golden sine algorithm, which successfully reduces the search space, shortens the convergence time, and improves local development capabilities. The algorithm's search ability is evaluated by simulating experiments on multiple single-modal and multi-modal benchmark test functions. The experimental results show that the improved algorithm outperforms the Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Dung Beetle Optimization (DBO), and its performance is significantly improved compared to the original TROA algorithm, verifying the effectiveness of the improved algorithm.