Toucan Optimization Algorithm

This paper proposes a swarm intelligence optimization algorithm inspired by the foraging behavior of toucans, called the Toucan Optimization Algorithm (TOA). This algorithm simulates the toucans' global exploration, local exploitation, collaborative sharing, and environmental adaptation capabilities during foraging in their natural environment, achieving efficient solutions to complex optimization problems. TOA utilizes adaptive jump step size, multi-scale beak accuracy, collaborative weights, and a history memory mechanism, combined with Lévy flight and local scanning strategies, to balance global search and local exploitation, maintaining stable convergence in continuous optimization, combinatorial optimization, and multi-objective optimization problems. This paper details the algorithm modeling, iterative formulas, and mathematical mechanisms of TOA, and analyzes its algorithmic characteristics, providing a theoretical basis for its application in engineering optimization, intelligent control, and data mining.