Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming

In this paper, we first refine a recently proposed metaheuristic called “Marriage in Honey-Bees Optimization” (MBO) for solving combinatorial optimization problems with some modifications to formally show that MBO converges to the global optimum value. We then adapt MBO into an algorithm called “Honey-Bees Policy Iteration” (HBPI) for solving infinite horizon-discounted cost stochastic dynamic programming problems and show that HBPI also converges to the optimal value.     

Investigation of the equality constraint effect on the reduction of the rotational ambiguity in three-component system using a novel grid search method

The obtained results by soft modeling multivariate curve resolution methods often are not unique and are questionable because of rotational ambiguity. It means a range of feasible solutions equally fit experimental data and fulfill the constraints. Regarding to chemometric literature, a survey of useful constraints for the reduction of the rotational ambiguity is a big challenge for chemometrician. It is worth to study the effects of applying constraints on the reduction of rotational ambiguity, since it can help us to choose the useful constraints in order to impose in multivariate curve resolution methods for analyzing data sets. In this work, we have investigated the effect of equality constraint on decreasing of the rotational ambiguity. For calculation of all feasible solutions corresponding with known spectrum, a novel systematic grid search method based on Species-based Particle Swarm Optimization is proposed in a three-component system.     

Data and modeling of negative ion transport in gases of interest for production of integrated circuits and nanotechnologies

We review techniques to prepare, evaluate and apply sets of cross section and transport data for negative ions that are required for the modeling of collisional non-equilibrium plasmas used for processing of microelectronic circuits. We collect and discuss the transport coefficients and cross section sets.We have compiled data for negative ions in CF₄ and CF₄-related negative ions in rare gases. In addition, we consider data for F⁻ and CF₃⁻ in rare gases. Furthermore, we analyze the cross sections of halogen negative ions in rare gases and other molecules. This is followed by the data for SF₆ related ions in SF₆ and in rare gases. The cross section for scattering of O⁻ in O₂ has been derived from the transport data and used to make calculations of the transport properties. Finally we give a brief discussion of the availability of the data for H⁻ ions in H₂. We have derived cross sections in several cases but the basic aim is to show the basic features of transport coefficients. In particular we discuss the need to represent properly some details such as the non-conservative nature of transport coefficients and the anisotropy of diffusion. Application of approximate theories and representations of cross sections are also discussed.     

Inverse transient radiation analysis in one-dimensional non-homogeneous participating slabs using particle swarm optimization algorithms

Particle Swarm Optimization (PSO) algorithms, including standard PSO, Stochastic PSO, and Multi-Phase PSO, are applied to solve the time-domain inverse transient radiation problems in the present research. Time-resolved transmittance and reflectance signals of four different measuring models serve as the measurement data, which estimate absorption, scattering coefficients, and geometric position within one-dimensional non-homogeneous media by inverse simulation. To check retrieval performances and accuracies of PSO-based approaches, four different inverse transient radiation cases are investigated to deal with one homogeneous layer, two-layer, three-layer, and continuous non-homogenous media. The influences of different searching ranges, swarm sizes, and maximum fly velocities on the fitness function of PSO are discussed. Meanwhile, the effects of measurement errors on the reconstruction accuracy are also investigated. All the results confirm that radiative parameters could be estimated accurately with measurement noise using PSO-based approaches.     

An interactive dynamic approach based on hybrid swarm optimization for solving multiobjective programming problem with fuzzy parameters

Real engineering design problems are generally characterized by the presence of many often conflicting and incommensurable objectives. Naturally, these objectives involve many parameters whose possible values may be assigned by the experts. The aim of this paper is to introduce a hybrid approach combining three optimization techniques, dynamic programming (DP), genetic algorithms and particle swarm optimization (PSO). Our approach integrates the merits of both DP and artificial optimization techniques and it has two characteristic features. Firstly, the proposed algorithm converts fuzzy multiobjective optimization problem to a sequence of a crisp nonlinear programming problems. Secondly, the proposed algorithm uses H-SOA for solving nonlinear programming problem. In which, any complex problem under certain structure can be solved and there is no need for the existence of some properties rather than traditional methods that need some features of the problem such as differentiability and continuity. Finally, with different degree of α we get different α-Pareto optimal solution of the problem. A numerical example is given to illustrate the results developed in this paper.     

Leadership Hijacking in Docker Swarm and Its Consequences

With the advent of microservice-based software architectures, an increasing number of modern cloud environments and enterprises use operating system level virtualization, which is often referred to as container infrastructures. Docker Swarm is one of the most popular container orchestration infrastructures, providing high availability and fault tolerance. Occasionally, discovered container escape vulnerabilities allow adversaries to execute code on the host operating system and operate within the cloud infrastructure. We show that Docker Swarm is currently not secured against misbehaving manager nodes. This allows a high impact, high probability privilege escalation attack, which we refer to as leadership hijacking, the possibility of which is neglected by the current cloud security literature. Cloud lateral movement and defense evasion payloads allow an adversary to leverage the Docker Swarm functionality to control each and every host in the underlying cluster. We demonstrate an end-to-end attack, in which an adversary with access to an application running on the cluster achieves full control of the cluster. To reduce the probability of a successful high impact attack, container orchestration infrastructures must reduce the trust level of participating nodes and, in particular, incorporate adversary immune leader election algorithms.     

Inverse acoustical characterization of natural jute sound absorbing material by the particle swarm optimization method

For modeling of jute as acoustic material, knowledge of its non-acoustical parameters like porosity, tortuosity, air flow resistivity, thermal and viscous characteristic lengths is a prime requisite. Measurement of these non-acoustical parameters is not straightforward and involves a dedicated measurement setup. So in order to overcome this issue, the inverse acoustical characterization can be used. In this paper, the particle swarm optimization method (PSO) is used as an optimization method. This method estimates the non-acoustical parameters of jute material in felt form by minimizing the error between experimental and theoretical sound absorption data. In this work, the impedance prediction models for fibrous materials like Johnson–Champoux–Allard model with rigid and limp frame and Garai–Pompoli model is used for sound absorption coefficient calculation by the transfer matrix method along with the PSO. The inverse estimated non-acoustical parameters for jute material are then compared with estimated and experimentally measured parameters for jute felts. Using these inversely predicted parameters, sound absorption of multilayer sound absorbers is also studied.     

改进人工蜂群算法优化ELM分类模型

针对极限学习机(Extreme Learning Machine,ELM)参数优化问题,提出改进人工蜂群算法(Improvement Artificial bee colony, IABC)优化ELM分类模型。算法采用解更新策略池代替固定不变的更新策略,将邻域搜索自适应化;优化侦察蜂搜索方式,利用Kent映射产生均匀性更优的初始随机数序列。在分类数据集中,将IABC-ELM分类模型同ELM、PSO-ELM分类模型进行对比实验。实验中,IABC-ELM模型取得了最佳的分类结果,得到了最低的输出权重范数。结果表明,IABC-ELM模型分类效果显著优于对比模型,证实了IABC算法优化ELM分类模型的有效性和优越性。     

改进的粒子群算法在虚拟企业合作伙伴选择中的应用

根据虚拟企业合作伙伴选择的原则,给出了合作伙伴选择的多目标决策模型,用定量的方法对合作伙伴进行描述.同时,给出了改进的二进制粒子群优化算法,并对本文的多目标优化问题进行求解.通过仿真试验测试,证明该方法是可行性.     

基于改进萤火虫算法的移动车辆参数识

提出了一种基于萤火虫优化算法的移动车辆参数直接识别方法.采用四自由度双轴十二参数车辆模型和欧拉梁有限元模型建立了车桥耦合系统的动力方程,并利用Newmark直接积分法求解了系统的动力响应.通过引入一种局部搜索策略和末位淘汰机制改进了萤火虫优化算法的收敛速率,并提高了识别结果的精度.本文方法仅利用车辆振动的竖向加速度响应测量就能进行移动车辆参数的识别.数值算例表明,改进的萤火虫优化算法可以准确地识别出车辆的质量、悬挂刚度和阻尼等参数,并且对测量噪声不敏感.