Hybrid ButterFlower Algorithm

This research introduces an innovative Hybrid ButterFlower Algorithm (HBFA) combining the Butterfly Optimization Algorithm (BOA) and the Sunflower Optimization Algorithm (SFO) to enhance search efficiency and convergence accuracy. BOA’s fragrance-based global and local search mechanisms are integrated with SFO’s attraction-repulsion dynamics to balance exploration and exploitation effectively. An adaptive control mechanism dynamically tunes the influence of each algorithm, preventing premature convergence and improving search diversity. Simulation results on 10 benchmark functions give insight into the hybrid algorithm’s ability to outperform both BOA and SFO in convergence speed, solution accuracy, and robustness across multimodal optimization problems.