Tree Frog Optimization Algorithm

Swarm optimization algorithms have shown significant advantages in solving high-dimensional, multimodal, and complex constrained optimization problems, but they still face challenges in practical applications such as premature convergence, local optimum traps, and insufficient adaptability to dynamic environments. This paper proposes a swarm optimization algorithm based on the behavior of rainforest tree frogs—the Tree Frog Optimization Algorithm (TFOA). By simulating the behaviors of tree frogs in the rainforest, such as jumping for food, local habitat selection, dynamic energy regulation, and pheromone-based environmental perception, TFOA achieves an effective balance between global exploration and local exploitation. This paper details the algorithm modeling, iterative process, mathematical formulas, and innovative mechanisms, and provides an in-depth analysis of the algorithm's characteristics. The results show that TFOA has significant potential in maintaining diversity, resisting local optima, and adaptive search capabilities, and can be widely applied to problems such as continuous optimization, combinatorial optimization, constrained optimization, and machine learning parameter tuning.