Abstract: Flower pollination algorithm is a new metaheuristic algorithm for optimization, however, it can have some stagnation and thus a lower convergence rate under certain conditions, which can limit the search ability of the algorithm. For this reason and to improve the search efficiency, this paper proposes a self-adaptive mutation operator in the process of local pollination, self-adaptive adjustment of the switch probability, and hybridization of the flower pollination algorithm with the firefly algorithm, which leads to a new hybrid approach called self-adaptive flower pollination algorithm enhanced by the firefly algorithm. The proposed approach has been validated by benchmark functions and has been compared with other algorithms such as DE-FPA and PSO-FPA. Results indicate that the proposed hybrid algorithm has a higher rate of convergence and stability than other algorithms.

Key words: Flower Pollination Algorithm（FPA）, Firefly Algorithm（FA）, optimal solution, switch probability; , mutation operator

摘要： 花授粉算法是一种新的启发式算法，由于存在易陷入局部最优且演化后期收敛速度慢等缺陷，导致算法的寻优能力受到限制。针对该算法存在的不足，在局部授粉过程中引入自适应的变异因子，并对花授粉算法中的转换概率进行自适应调整后，将其与萤火虫算法相结合，提出了一种基于萤火虫算法的改进花授粉算法;最后，通过经典的标准测试函数对新提出的算法与DE-FPA、PSO-FPA做比较实验。实验结果表明，改进后的算法比基本花授粉算法具有更高的收敛精度和稳定性。

关键词: 花授粉算法, 萤火虫算法, 最优解, 转换概率, 变异因子