Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework,first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

引入Lévy flight和萤火虫行为的鱼群算法

针对人工鱼群算法(artificial fish-swarm algorithm,AFSA)和萤火虫算法(firefly algorithm,FA)在多维多极值函数寻优过程中易陷入局部最优和精度有待提高等问题,提出引入Lévy flight和萤火虫行为的鱼群算法(fish swarm algorithm with Lévy flight and firefly behavior).该算法将萤火虫算法中萤火虫个体的移动策略引入到鱼群的聚群,觅食两种行为模式中,进而将Lévy flight引入到鱼群的搜索策略中,使得鱼群的搜索更加高效.此外,采取一种基于动态参数的非线性变视野和变步长的策略来限定鱼群的搜索范围.仿真分析表明,新算法较其他测试算法具有更好的全局搜索能力和寻优精度.

Control allocation for a class of morphing aircraft with integer constraints based on Lévy flight

Aiming at tracking control of a class of innovative control effector(ICE) aircraft with distributed arrays of actuators, this paper proposes a control allocation scheme based on the Lévy flight.Different from the conventional aircraft control allocation problem,the particular characteristic of actuators makes the actuator control command totally subject to integer constraints. In order to tackle this problem, first, the control allocation problem is described as an integer programming problem with two desired objectives. Then considering the requirement of real-time, a metaheuristic algorithm based on the Lévy flight is introduced to tackling this problem. In order to improve the searching efficiency, several targeted and heuristic strategies including variable step length and inherited population initialization according to feedback and so on are designed. Moreover, to prevent the incertitude of the metaheuristic algorithm and ensure the flight stability, a guaranteed control strategy is designed. Finally, a time-varying simulation model is introduced to verifying the effectiveness of the proposed scheme. The contrastive simulation results indicate that the proposed scheme achieves superior tracking performance with appropriate actuator dynamics and computational time, and the improvements for efficiency are active and the parameter settings are reasonable.

基于Lévy Flight的混合GA在柔性作业车间调度问题中的性能分析

近年来,柔性作业车间调度问题(FJSP)由于其NP难特性与在制造系统中的广泛应用被大量关注。为提高该类问题求解效率,本文在标准Lévy flight的基础上提出了一种新的离散Lévy flight搜索策略,并将该策略与遗传算法框架结合,形成一种离散Lévy flight策略的混合遗传算法。该混合算法通过使用离散Lévy flight搜索策略对每代精英种群进行变步长搜索,提高了算法的局部搜索能力,增强了种群多样性。本文通过将CS、GA和TLBO等经典算法作为对比算法,对不同规模的54个FJSP算例进行实验,证明了所提出的算法具备更好的收敛效果与稳定性,适合于求解大规模FJSP。

基于Lévy flight的自适应动态增强烟花算法

为综合解决传统烟花算法爆炸半径可能为零导致资源浪费以及增强烟花算法引入的最小爆炸半径检测机制导致局部搜索能力较弱的问题,针对增强烟花算法提出了两种改进策略:引入自适应动态半径调整策略改进爆炸半径,根据不同阶段的启发式信息,即当前最优烟花距离其他烟花位置的信息,动态调整爆炸半径的大小来平衡全局和局部搜索能力,该策略可以使算法后期爆炸半径缩小到较小值进行细致的局部搜索;引入具有较强随机性的莱维飞行策略改进爆炸产生火花位置的方式,增强局部搜索的多样性。采用12个标准测试函数及其偏移函数进行实验,相比增强烟花算法,改进后的算法提高了标准函数及其偏移函数的寻优精度,在高维复杂的优化问题上具有较好的收敛能力。

基于Lévy Flight的地震搜救模拟研究

地震是一种极其严重的自然灾害。由于地震灾害救援密切关系到灾区人民的生命财产安全,关于灾后救援的科学调查研究因此也具有实践指导意义。搜救行动是一个需要合作的过程。探讨合作搜救的复杂特性,对指导救援行动意义重大。随机游走模型在众多领域中已经得到了广泛的应用。将随机游走模型中的一种,Lévy Flight引入到搜救模拟的研究中来,建立基于合作的搜救模型。基于初始救援物资有限的前提,将被困幸存者存活率这一因素加以考虑,提出了救灾物资这一概念,并与幸存者存活率相结合。探讨在初期物资有限的前提下,不同规模的救援队配置对救援工作开展的影响以及新的合作规则在救援中发挥的作用。结果表明,模型能在一定程度上描述灾后搜救过程,对加深搜救工作的理解及对策制定有一定的指导作用。

Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy

In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.

基于混沌精英和Lévy飞行策略的鲸鱼优化算法

针对鲸鱼优化算法(Whale Optimization Algorithm,WOA)存在的收敛速度慢、精度低的问题,提出了基于Tent混沌精英和Lévy飞行策略的鲸鱼优化算法(TELWOA)。使用Tent混沌映射初始化鲸鱼种群,保持种群的多样性,并通过引入精英反向学习策略,对初始种群的精英个体生成反向解,选取适应度高的种群作为下一代鲸鱼种群,加快算法收敛速度。其次,通过使用非线性收敛因子,缓解算法全局搜索和局部搜索能力不平衡的现象。最后,在鲸鱼位置寻优过程中使用Lévy飞行策略,避免算法陷入局部最优,提升算法的全局搜索能力。通过对不同改进策略的有效性分析、与其他智能算法的对比分析,证明了TELWOA算法在收敛精度、算法稳定性和全局寻优能力上与对比算法有显著提升,具有一定的实际工程应用能力。

融合学习差异与Lévy飞行的动态平衡正余弦算法

为了提升正余弦算法的收敛性能,本文提出一种融合学习差异与Lévy飞行的动态平衡正余弦改进算法,定义为SCALLD算法。通过引入学习差异策略,减少搜索个体对其位置信息的依赖,增强全局探索能力;加入Lévy飞行机制,丰富种群多样性,提升探索能力;采用动态平衡策略,平衡探索与开发能力,提高收敛速度和稳定性。在CEC2022基准测试函数上的实验表明,与六种算法相比,SCALLD展现出更优的收敛性能和稳定性,Wilcoxon秩和检验进一步证明了SCALLD的竞争优势,为解决复杂优化问题提供参考。

由Lévy过程驱动的加权自排斥扩散的长时间行为和统计推断

假设是一个跳有界且界限为1的Lévy过程,生成三元组为。在本文中,我们考虑了由Lévy过程驱动的线性自排斥扩散方程,其中,和。这类过程是一类自交互扩散过程。我们研究了当t趋于无穷时解的长时间行为,发现它具有一种循环收敛性,这在此前的研究中尚未有类似的结论。进一步的,当w=0时在连续观测情况下,通过最小二乘法给出了方程参数的估计。我们证明了的估计量具有强相合性,并讨论了它的渐近分布。

Presentation of the Berry-Tabor conjecture in Levy plates

The Berry-Tabor(BT)conjecture is a famous statistical inference in quantum chaos,which not only establishes the spectral fluctuations of quantum systems whose classical counterparts are integrable but can also be used to describe other wave phenomena.In this paper,the BT conjecture has been extended to Lévy plates.As predicted by the BT conjecture,level clustering is present in the spectra of Lévy plates.The consequence of level clustering is studied by introducing the distribution of nearest neighbor frequency level spacing ratios P(r),which is calculated through the analytical solution obtained by the Hamiltonian approach.Our work investigates the impact of varying foundation parameters,rotary inertia,and boundary conditions on the frequency spectra,and we find that P(r)conforms to a Poisson distribution in all cases.The reason for the occurrence of the Poisson distribution in the Lévy plates is the independence between modal frequencies,which can be understood through mode functions.

Symmetric Brownian motor subjected to Lévy noise

In the past few years,attention has mainly been focused on the symmetric Brownian motor(BM)with Gaussian noises,whose current and energy conversion efficiency are very low.Here,we investigate the operating performance of the symmetric BM subjected to Lévy noise.Through numerical simulations,it is found that the operating performance of the motor can be greatly improved in asymmetric Lévy noise.Without any load,the Lévy noises with smaller stable indexes can let the motor give rise to a much greater current.With a load,the energy conversion efficiency of the motor can be enhanced by adjusting the stable indexes of the Lévy noises with symmetry breaking.The results of this research are of great significance for opening up BM’s intrinsic physical mechanism and promoting the development of nanotechnology.

Spatial patterns of the Brusselator model with asymmetric Lévy diffusion

The formation of spatial patterns is an important issue in reaction–diffusion systems.Previous studies have mainly focused on the spatial patterns in reaction–diffusion models equipped with symmetric diffusion(such as normal or fractional Laplace diffusion),namely,assuming that spatial environments of the systems are homogeneous.However,the complexity and heterogeneity of spatial environments of biochemical reactions in vivo can lead to asymmetric diffusion of reactants.Naturally,there arises an open question of how the asymmetric diffusion affects dynamical behaviors of biochemical reaction systems.To answer this,we build a general asymmetric L´evy diffusion model based on the theory of a continuous time random walk.In addition,we investigate the two-species Brusselator model with asymmetric L´evy diffusion,and obtain a general condition for the formation of Turing and wave patterns.More interestingly,we find that even though the Brusselator model with symmetric diffusion cannot produce steady spatial patterns for some parameters,the asymmetry of L´evy diffusion for this model can produce wave patterns.This is different from the previous result that wave instability requires at least a three-species model.In addition,the asymmetry of L´evy diffusion can significantly affect the amplitude and frequency of the spatial patterns.Our results enrich our knowledge of the mechanisms of pattern formation.

LÉVY AREA ANALYSIS AND PARAMETER ESTIMATION FOR FOU PROCESSES VIA NON-GEOMETRIC ROUGH PATH THEORY

This paper addresses the estimation problem of an unknown drift parameter matrix for a fractional Ornstein-Uhlenbeck process in a multi-dimensional setting.To tackle this problem,we propose a novel approach based on rough path theory that allows us to construct pathwise rough path estimators from both continuous and discrete observations of a single path.Our approach is particularly suitable for high-frequency data.To formulate the parameter estimators,we introduce a theory of pathwise Itôintegrals with respect to fractional Brownian motion.By establishing the regularity of fractional Ornstein-Uhlenbeck processes and analyzing the long-term behavior of the associated Lévy area processes,we demonstrate that our estimators are strongly consistent and pathwise stable.Our findings offer a new perspective on estimating the drift parameter matrix for fractional Ornstein-Uhlenbeck processes in multi-dimensional settings,and may have practical implications for fields including finance,economics,and engineering.

Lévy噪声和高斯白噪声驱动的非对称三稳系统的相转移问题研究

讨论了非对称三稳系统在Lévy噪声和高斯白噪声共同驱动下的相转移问题。采用四阶Runge-Kutta算法,计算了系统的稳态概率密度函数,通过改变系统参数和噪声参数观察其稳态概率密度函数曲线形态的变化情况。研究发现,非对称参数、加性噪声强度、乘性噪声强度、稳定性指标、偏斜参数均可诱导系统相转移,当分别改变加性噪声强度和乘性噪声强度时,概率密度函数的峰数与高度的变化情况相反。此外,在相同的非对称参数下,随着偏斜参数正负取值的变化,概率密度曲线图中峰的结构亦呈现不同的现象。

具有Lévy跳的随机时滞食饵-捕食模型的最优收获

研究了一类带Lévy跳的时滞食饵-捕食模型的捕捞策略.利用比较原理分析物种平均持久性和灭绝性的充分条件,在一定假设条件下证明了该模型分布的稳定性,运用遍历法建立最优收获策略存在的条件,得到了最优捕捞努力量和最大持续产量.最后通过数值模拟验证理论结果.

乘性Lévy噪声驱动的随机传染病模型参数估计

利用最小二乘方法研究由乘性Lévy噪声驱动的随机易感潜伏—感染-治愈-易感(susceptible exposed-infectious-recovered-susceptible,SEIRS)传染病模型的参数估计问题.首先,基于模型的离散观测样本,得到模型中未知参数的最小二乘估计量;其次,讨论当噪声强度趋于0且离散观测样本量趋于无穷时估计量的渐近一致性,并给出估计量的极限分布;最后,选定不同的噪声强度和样本量进行数值模拟.结果表明:参数估计量的相对误差随样本量增多或噪声强度减小而不断降低,当样本量足够大且噪声强度足够小时估计量几乎接近真实值.

具有Lévy跳跃驱动的Rosenzweig-MacArthur捕食者–食饵模型的渐近行为

本文主要利用随机分析等相关知识,根据进化稳定策略框架对Rosenzweig-MacArthur捕食者–食饵模型进行改进,证明了具有Lévy跳跃驱动的Rosenzweig-MacArthur捕食者–食饵模型解的存在唯一性和随机有界性,并探究了该模型的灭绝性、持久性和平稳分布。

Lévy飞行的正余弦乌燕鸥混合算法及应用

为解决标准乌燕鸥算法(STOA)易陷入局部最优和收敛速度慢等缺点,提出一种混合正余弦算法(SCA)和Lévy飞行的自适应乌燕鸥算法(SLSTOA)。采用正余弦算法的搜索方式,同时采用非线性递减自适应正弦因子,改进乌燕鸥算法的攻击搜索方式,来增强STOA算法的全局与局部探索能力。乌燕鸥个体和最优个体通过Lévy飞行策略进行变异,来增加种群多样性和扩大搜索空间,以达到提高跳出局部最优和全局探索能力。与四种先进的元启发式算法比较,SLSTOA算法性能通过6个基准测试函数进行评价,结果表明,相比其他四种元启发式算法,SLSTOA算法精度高、稳定性好和鲁棒性强。同时为验证SLSTOA算法的科学性与实用性,将其应用于解决32t/22.5m桥式起重机主梁结构优化设计中。