分段搜索的果蝇算法及其对纺织企业资源配置

Fruit Fly Optimization Algorithm Based on Segmented Search and Resource Allo cation to Textile Enterprise

为了解决纺织企业智能化资源配置中的多参数多目标优化问题,提出了Multi-P-LevyFOA。首先,建立纺织企业智能化转型的多参数多目标资源配置模型。然后,在迭代次数小于等于2/3总迭代数时,基于Levy飞行的随机数更新种群位置,扩大搜寻范围,避免陷入局部最优。在迭代大于2/3总次数时,使用均匀分布的随机数更新种群位置,缩小搜寻范围,避免跳出最优值范围。对算法在不同迭代时刻全局和局部寻优能力进行了分析,对Multi-P-LevyFOA的算法时间复杂度进行了分析并与标准FOA和5个改进的FOA进行比较,并证明了其收敛性。将Multi-P-LevyFOA与其他4种改进的FOA进行了性能对比,重点分析了算法阈值SEP在22个benchmark函数上的影响,研究了Levy飞行中β参数对寻优效果的影响规律。最后,以陕西某纺织企业的智能化资源配置为例,验证了Multi-P-LevyFOA的可行性。实验结果表明,Multi-P-LevyFOA能够有效地解决多参数多目标优化问题,为纺织企业智能化资源配置提供参考。

To solve the multi-parameter and multi-objective optimization problem in the intelligent resource allocation of textile enterprises,Multi-P-LevyFOA is proposed.Firstly,a multi-parameter and multi-objective resource allocation model for intelligent transformation of textile enterprises is established.Then,if the number of iterations is less than or equal to 2/3 of the total number of iterations,based on the random number of Levy flight,the population position is updated to expand the search range and avoid falling into local optimization.When the number of iterations is greater than 2/3 of the total number,the population position is updated with random numbers of uniform distribution,and the search range is narrowed to avoid jumping out of the optimal value range.The global and local optimization ability of the algorithm at different iteration times are analyzed,and the time complexity of Multi-P-LevyFOA algorithm is analyzed and compared with standard FOA and five improved FOA,and its convergence is proven.The performance of Multi-P-LevyFOA is compared with other four improved FOA,the influence of threshold SEP of the algorithm on the 22 benchmark functions is analyzed,and the influence law ofβparameter on the optimization effect of the algorithm in Levy flight is studied.Finally,taking the intelligent resource allocation of a textile enterprise in Shaanxi province as an example,the feasibility of Multi-P-LevyFOA is verified.Experimental results show that the Multi-P-LevyFOA can effectively solve the multi-parameter and multi-objective optimization problem under appropriateβparameters,which provides a reference for the intelligent resource allocation of textile enterprises.