In recent years,the three dimensional reconstruction of vascular structures in the field of medical research has been extensively developed.Several studies describe the various numerical methods to numerical modeling ...In recent years,the three dimensional reconstruction of vascular structures in the field of medical research has been extensively developed.Several studies describe the various numerical methods to numerical modeling of vascular structures in near-reality.However,the current approaches remain too expensive in terms of storage capacity.Therefore,it is necessary to find the right balance between the relevance of information and storage space.This article adopts two sets of human retinal blood vessel data in 3D to proceed with data reduction in the first part and then via 3D fractal reconstruction,recreate them in a second part.The results show that the reduction rate obtained is between 66%and 95%as a function of the tolerance rate.Depending on the number of iterations used,the 3D blood vessel model is successful at reconstruction with an average error of 0.19 to 5.73 percent between the original picture and the reconstructed image.展开更多
针对鲸鱼优化算法(whale optimization algorithm,WOA)易陷入局部最优,收敛速度慢和寻优精度低等问题,提出一种融合混沌映射和二次插值的自适应鲸鱼优化算法(adaptive whale optimization algorithm based on chaotic mapping and quadr...针对鲸鱼优化算法(whale optimization algorithm,WOA)易陷入局部最优,收敛速度慢和寻优精度低等问题,提出一种融合混沌映射和二次插值的自适应鲸鱼优化算法(adaptive whale optimization algorithm based on chaotic mapping and quadratic interpolation,CQAWOA)。引入混沌映射在初始化阶段生成新种群,实现种群多样性;设计自适应权重,提高算法全局搜索和局部寻优能力并加快收敛速度;利用二次插值策略生成新的鲸鱼个体,采用贪婪策略更新局部最优解,提高种群计算的精度。通过15个基准函数将改进算法与其它优化算法进行对比测试,测试结果验证了在求解过程中,改进算法寻优速度和求解精度均存在显著提升。展开更多
Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its...Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.展开更多
文摘In recent years,the three dimensional reconstruction of vascular structures in the field of medical research has been extensively developed.Several studies describe the various numerical methods to numerical modeling of vascular structures in near-reality.However,the current approaches remain too expensive in terms of storage capacity.Therefore,it is necessary to find the right balance between the relevance of information and storage space.This article adopts two sets of human retinal blood vessel data in 3D to proceed with data reduction in the first part and then via 3D fractal reconstruction,recreate them in a second part.The results show that the reduction rate obtained is between 66%and 95%as a function of the tolerance rate.Depending on the number of iterations used,the 3D blood vessel model is successful at reconstruction with an average error of 0.19 to 5.73 percent between the original picture and the reconstructed image.
文摘针对鲸鱼优化算法(whale optimization algorithm,WOA)易陷入局部最优,收敛速度慢和寻优精度低等问题,提出一种融合混沌映射和二次插值的自适应鲸鱼优化算法(adaptive whale optimization algorithm based on chaotic mapping and quadratic interpolation,CQAWOA)。引入混沌映射在初始化阶段生成新种群,实现种群多样性;设计自适应权重,提高算法全局搜索和局部寻优能力并加快收敛速度;利用二次插值策略生成新的鲸鱼个体,采用贪婪策略更新局部最优解,提高种群计算的精度。通过15个基准函数将改进算法与其它优化算法进行对比测试,测试结果验证了在求解过程中,改进算法寻优速度和求解精度均存在显著提升。
文摘Though the Butterfly Bptimization Algorithm(BOA)has already proved its effectiveness as a robust optimization algorithm,it has certain disadvantages.So,a new variant of BOA,namely mLBOA,is proposed here to improve its performance.The proposed algorithm employs a self-adaptive parameter setting,Lagrange interpolation formula,and a new local search strategy embedded with Levy flight search to enhance its searching ability to make a better trade-off between exploration and exploitation.Also,the fragrance generation scheme of BOA is modified,which leads for exploring the domain effectively for better searching.To evaluate the performance,it has been applied to solve the IEEE CEC 2017 benchmark suite.The results have been compared to that of six state-of-the-art algorithms and five BOA variants.Moreover,various statistical tests,such as the Friedman rank test,Wilcoxon rank test,convergence analysis,and complexity analysis,have been conducted to justify the rank,significance,and complexity of the proposed mLBOA.Finally,the mLBOA has been applied to solve three real-world engineering design problems.From all the analyses,it has been found that the proposed mLBOA is a competitive algorithm compared to other popular state-of-the-art algorithms and BOA variants.