基于多策略的改进花授粉算法

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...

Improved Flower Pollination Algorithm Based on Multi-strategy

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.