Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (23): 94-108.DOI: 10.3778/j.issn.1002-8331.1808-0168

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Flower pollination algorithm with new pollination methods

DUAN Yanming, XIAO Huihui, LIN Fang   

  1. College of Computer and Information Engineering, Hechi University, Hechi, Guangxi 546300, China
  • Online:2018-12-01 Published:2018-11-30


段艳明,肖辉辉,林  芳   

  1. 河池学院 计算机与信息工程学院,广西 河池 546300

Abstract: In order to solve the problems of low accuracy computation, slow velocity convergence and premature convergence caused by the shortcomings of search equations of the flower pollination algorithm, it proposes a Flower Pollination Algorithm with New pollination Methods(NMFPA). The global pollination in the algorithm is composed of the inertia weight and two groups of random individual difference vectors, to maintain the diversity of the population and improve the global exploration ability of the algorithm. A new local pollination strategy is constructed by using the information sharing mechanism and two new mutation strategies to enhance the local development ability of the algorithm, the optimal individuals based on Gauss mutation are used to guide the evolution direction of the other populations which reduces the blindness of individual evolution and improves the convergence speed and precision of the algorithm, and non-uniform mutation mechanism is introduced to increase the diversity of the population that avoids the algorithm is easy to fall into local extreme points and enhances the overall performance of the algorithm. The numerical simulation experiments are carried out on 22 test functions. The experimental results and statistical analysis show that the new algorithm has higher convergence precision and speed compared with the standard FPA, and can solve the premature problem better. In addition, compared with the improved FPA algorithm from many perspectives, the experimental results show that the improved algorithm proposed in this paper is a new and competitive algorithm. Meanwhile, the NMFPA algorithm is used to solve the permutation flow shop scheduling problem. The experimental results show that the new algorithm is feasible and has certain advantages for solving practical engineering problems.

Key words: Flower Pollination Algorithm(FPA), inertia weight, non-uniform mutation, convergence ability, population diversity

摘要: 为了解决因花授粉算法搜索方程存在的不足所导致的易早熟、后期收敛速度慢和寻优精度低的问题,提出了一种新授粉方式的花授粉算法(Flower Pollination Algorithm with New pollination Methods,NMFPA)。该算法把惯性权重和两组随机个体差异矢量融入到全局搜索,组成新的全局授粉,以保持种群的差异性,提高算法的全局探索能力;利用信息共享机制与两种新的变异策略构建新局部授粉策略,增强算法的局部开发能力;为了减少个体进化的盲目性,提高算法的收敛速度和精度,采用基于高斯变异的最优个体来引导其他种群个体的进化方向,并且引入非均匀变异机制增加种群的多样性,避免算法易陷入局部极值点,提升算法的全局优化性能。在22个测试函数上进行数值仿真实验,实验结果和统计分析验证了新算法较标准FPA算法,在收敛精度和速度上有明显提升,且能够较好地解决早熟问题。此外,与已有改进的FPA算法从多角度进行对比分析,实验结果表明改进算法是一种富有竞争力的新算法。同时,运用NMFPA算法求解置换流水车间调度问题,实验结果验证了新算法用于解决实际工程问题是可行的,且具有一定的优势。

关键词: 花授粉算法, 惯性权重, 非均匀变异, 收敛能力, 多样性