Cockatiel Optimization Algorithm

With the rapid development of computational intelligence technology, various swarm intelligence optimization algorithms have shown good performance in engineering optimization, combinatorial optimization, and solving complex systems. However, existing optimization algorithms still suffer from slow convergence speed, susceptibility to local optima, and insufficient global search capability when facing high-dimensional, multi-modal, or nonlinear complex problems. To address this, this paper proposes a novel swarm intelligence optimization algorithm based on the behavioral characteristics of cockatiels—the Cockatiel Optimization Algorithm (COA). This algorithm draws on the cockatiel's flocking flight behavior, vocal communication, and environmental adaptation mechanisms, achieving efficient search of the solution space through individual experience attraction, global optimum guidance, and neighbor information sharing. The paper details the algorithm's mathematical model, position update strategy, group information transmission mechanism, and escape strategy, and verifies its superior performance on standard test functions through a series of numerical experiments. Experimental results show that COA outperforms traditional swarm intelligence algorithms in global search, convergence speed, and multi-modal function optimization capability.