混合花粉算法求解全局优化问题

元启发式算法可以用作寻找近似最优解的有效工具，因此，对元启发式算法进行改进，提高算法性能是有必要的。本文介绍花粉算法(Flower Pollination Algorithm, FPA)的增强变体，将花粉算法与极值优化算法(Extremal Optimization, EO)混合形成FPA-EO算法。FPA-EO算法综合利用了FPA的全局搜索能力和EO的局部搜索能力，并将其应用于11个基准测试函数来测试新算法。同时将该算法与其他4种著名优化算法（标准花粉算法(FPA)、蝙蝠算法(BAT)、萤火虫算法(FA)、模拟退火算法(SA)）进行比较。综合结果表明，本文算法能够找到比其他4种算法更精确的解。

Hybrid Flower Pollination Algorithm For Solving Global Optimization Problems

Metaheuristic algorithms can be used as an effective tool for finding near-optimal solutions. Therefore, it is necessary to improve metaheuristic algorithms and enhance the algorithm’s performance. This paper introduces an enhanced variant of Flower Pollination Algorithm (FPA)，which combines FPA with Extremal Optimization(EO) to form the FPA-EO. The FPA-EO algorithm makes use of the global search capability of FPA and the local search capability of EO, and applies it to 11 benchmark functions to test the new algorithm. At the same time, the algorithm is compared with other four famous optimization algorithms: standard flower pollination algorithm (FPA), bat algorithm (BAT), firefly algorithm (FA), and simulated annealing algorithm(SA). The comprehensive results show that the algorithm can find a more accurate solution than the other four algorithms.