Red-Eyed Tree Frog Optimization Algorithm

Swarm intelligence optimization algorithms are a class of methods that solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes an optimization algorithm based on the behavior of the red-eyed tree frog—the Red-Eyed Tree Frog Optimization Algorithm (RETFOA). This algorithm simulates the foraging, local patrolling, and reproductive behaviors of the red-eyed tree frog in a rainforest environment, achieving a balance between global search and local exploitation. Through adaptive jumping mechanisms, multimodal information sharing mechanisms, environmentally aware weights, and elite breeding strategies, the algorithm demonstrates strong convergence ability, resistance to local optima, and dynamic adaptability in continuous optimization, combinatorial optimization, and multi-objective optimization. This paper provides a detailed analysis of the algorithm's model establishment, iterative process, mathematical formula derivation, and characteristics, and discusses its applications and extensions.