摘要:花授粉算法是近年来提出的一种新型的、简单高效的优化算法，已在各个领域得到广泛应用，但其搜索策略存在的不足，制约着其应用范围.为此，提出一种改进的基于多策略的花授粉算法.首先，新全局搜索策略通过利用两组随机个体差异矢量和莱维飞行机制来增加种群多样性并扩大搜索范围，使算法更易跳出局部最优，提升其开采能力；其次，在局部搜索部分引入精英变异策略，并与随机个体变异机制组合成一种新的局部授粉策略，利用精英个体对其他个体的演化方向进行引导，提高算法的搜索速度；通过随机个体变异策略来保持种群的多样性，增强算法的持续优化能力；同时，通过一种线性递减概率规则调节这两种变异策略，使其取长补短，以提高算法的优化能力；最后，对进化中没有得到改善的解，利用余弦函数搜索因子策略产生一个新解加以替换，从而提高算法解的质量.通过5类经典测试函数的仿真实验和采用统计学上的分析，证明了该算法的稳定性和有效性；与现有经典的和知名的改进算法进行了对比，实验结果表明，所提出的改进算法是一种富有竞争力的新算法.同时，利用改进算法对军事领域中的无人作战飞行器航线规划问题进行求解，测试结果表明，改进算法在解决实际工程问题时，同样具有一定的优势.



Abstract:The flower pollination algorithm (FPA) is a novel, easy and efficient optimization algorithm proposed in recent years. It has been widely used in various fields, but its search strategy has some defects, which become an impediment to its application. Therefore, this paper introduces an improved flower pollination algorithm based on multi-strategy. First, the new global search strategy was adopted through two groups of random individual difference vectors and Lévy flight to increase the diversity of population and expand the search range, making the algorithm easier to escape the local optimum and improve its exploitation ability. Second, the elite mutation strategy was used in the local search, and a new local pollination strategy was developed by combing it with the random individual mutation mechanism. The elite individuals were used to guide the evolution direction of other individuals and improve the search speed of the algorithm. The random individual mutation strategy was adopted to keep the population diverse and enhance the continuous optimization capability of the algorithm. In addition, the two mutation strategies were adjusted through linear decreasing probability rule to make them complement with each other and improve the optimization capability of the algorithm. Finally, a new solution was generated by the cosine function search factor strategy to replace the unimproved solution and improve the quality of the solution. The stability and effectiveness of the algorithm were proved by simulation experiments of 5 kinds of classical test functions and statistical analysis. The experimental results show that the improved algorithm proposed in this paper is a novel and competitive algorithm compared with the existing classical and state-of-the-art improved algorithms. At the same time, the proposed algorithm was used to solve the route planning problem of unmanned combat aerial vehicle (UCAV) in the military field. The test results show that the proposed algorithm also has certain advantages in solving practical engineering problems.



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