A new flower pollination algorithm with improved convergence and its application to engineering optimization

The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm that mimics the pollination behaviour of flowering plants. Despite the promising performance of FPA in solving single objective optimization problems, its convergence still poses challenges in practice. This study proposes a modified FPA with additional features from chaos theory and frog leaping algorithm augmented by inertia weights. The modified FPA proposed in this study is tested against benchmark mathematical functions, mechanical engineering design optimization problems, and machining process optimization problems. Performance comparison with other state-of-the-art optimization algorithms has demonstrated its ability in terms of convergence. The modified FPA significantly reduced the number of function evaluations by 84.14%, as compared to FPA in optimizing the benchmark mathematical functions. Besides, the proposed modified FPA outperformed others in 12 out of 15 mechanical engineering optimization problems. •We propose a novel modified flower pollination algorithm (MFPA) for global optimization.•Its effectiveness is tested in a series of benchmark test function, mechanical design problems and process optimization.•Performance comparison with other state-of-the-art method is made.•The proposed MFPA shows better performance than others in terms of convergence rate and stability.