Mavis Bird Optimization Algorithm

Swarm optimization algorithms, by simulating the collective behavior of organisms in nature, have achieved efficient solutions to complex optimization problems and have been widely applied in engineering optimization, combinatorial optimization, and high-dimensional continuous optimization in recent years. This paper proposes an optimization algorithm based on the behavior of the Mavis Bird Optimization (MBO), which simulates the foraging, singing, migration, and courtship behaviors of the Mavis Bird to achieve swarm intelligent collaborative search. In the algorithm modeling, this paper introduces chaotic perturbation, adaptive step size, and elite guidance strategies, and describes the global search, local development, and information sharing mechanisms of the algorithm in detail through pure text mathematical formulas. Theoretical analysis shows that the MBO algorithm has significant advantages in global exploration ability, local fine-tuning ability, and swarm cooperation ability, and is suitable for high-dimensional complex optimization problems.