A Novel Butterfly Cuckoo Hybrid Algorithm for Numerical Optimization

Swarm intelligence (SI) algorithms are applied in fields like communication routing optimization, node coverage, signal prediction, etc. Butterfly optimization algorithm, a new method, has attracted attention, but it has the shortcomings of slow convergence speed and local optimal risk. To enhance the BOA's performance, the Butterfly Cuckoo Hybrid Algorithm (BCHA) was proposed. In addition, a dynamic adjustment factor β was introduced in the optimization process to adapt to the search range of the individual, thereby upgrading the convergence accuracy and global search ability of the BCHA. The proposed algorithm's performance was validated by multiple standard numerical optimization functions, which was compared to PSO, BOA, GWO, and GWOCS algorithms. The simulation experiment results demonstrated that the BCHA has high convergence accuracy and stability compared to other approaches. Besides, communication sensor nodes optimization deployment problem will be addressed by a further enhanced BCHA in future work.