Individual-based multi-objective optimal structured treatment interruption for HIV infection

In this paper, based on the measurable quantities from an individual patient that has infection to human immunodeficiency virus (HIV) and his/her condition is near to acquired immune deficiency syndrome (AIDS), individual-based multi-objective optimal treatments have been proposed. Firstly, the most effective parameters of the patient in computing Long-term non-progressor (LTNP) equilibrium are derived using global sensitivity analysis (GSA). To accomplish GSA effectively, Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) are utilized to rank each of the parameters based on each state of the 5-dimensional model. Then, these results are used by Dempster–Shafer (D–S) evidence theory (DSET) to rank the most effective parameters comprehensively. Now, these effective identified parameters are estimated using extended Kalman filter (EKF), which its covariance matrices are optimized based on particle swarm optimization (PSO) algorithm. Thus, the proposed methodology gives a calibrated model corresponding to the individual patient. Based on this calibrated model, the LTNP equilibrium related to the individual patient is derived. Using the derived individual-based LTNP equilibrium optimal structured treatment interruption (STI) strategies are extracted by defining suitable multi-objective optimization problem and solving it through using non-dominated sorting genetic algorithm-II (NSGA-II). The results demonstrate that the proposed optimal treatments are able to effectively reach LTNP equilibrium with using the minimum and maximum drug usage of 3.6% and 35.1% of full drug usage treatment. Meanwhile, the different optimal treatments give the decision-makers enough flexibility to choose the suitable treatment based on existing facilities and necessities.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.     

自适应天牛须优化与K均值聚类的图像分割算法研究

在机器识别中,图像分割是重要的一个步骤,传统分割手段存在一定缺陷。针对传统K均值聚类分割的初始聚类中心敏感的缺陷进行了优化,利用自适应天牛须优化算法,避免了这一问题。通过实验结果表明,该算法(ABASK)对图像进行分割,既可以保证图像轮廓的分割,同时还可以更多地保留图像细节。     

Loss cost reduction and power quality improvement with applying robust optimization algorithm for optimum energy storage system placement and capacitor bank allocation

Power losses cause the underutilization of distributed generation (DG) units in addition to the cost increasing in microgrid. Minimizing these losses has been focused in many papers. Using energy storage system (ESS) is a crucial solution for loss reduction. ESS can balance the power exchange in on-peak times where its location and size optimization can improve the microgrid efficiency and reduce the loss cost significantly. Moreover, to ensure the power quality by improving the voltage profile, capacitor bank can be installed optimally on some buses. Optimization of size and location of the capacitor bank can enhance the reactive power that is leading to power loss reduction. In other words, the capacitor bank is applied to compensate the total reactive power and consequently, the current is reduced that results in power loss reduction. In this article, the problem is defined as the optimum location and size of ESS and capacitor bank in the microgrid. Due to the complexity of the problem in many options for selecting the buses to implement these elements (ESS and capacitor bank), robust approach using the particle swarm optimization algorithm and general algebraic modeling system are applied for optimization process. In addition, the uncertainty of renewable DGs such as photovoltaic and wind turbine is modeled by probability density functions and Monte-Carlo is used for selecting more probable cases in optimization processes. The results show the loss cost reduction and improvement in voltage and power profile with less fluctuations and more stability.     

Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products

This paper analyzes the model of the recycled products which are considered with the minimum quality level in manufacturing/remanufacturing system. In this model, a constant demand is satisfied by manufacturing raw materials and remanufacturing recycled products which are up to the quality level. It is assumed that functions of recycling rate, buyback cost and remanufacturing cost are depend on the minimum quality level. The quality level of recycled products is set to be exponential distribution and then the model is established. The results show that when the buyback cost is low (the quality of the recycled products is low), the average total cost is low, though the remanufacturing cost is high. Namely, the companies are willing to recycle the used products with low quality level. Meanwhile, the optimal strategy of recycling, manufacturing and remanufacturing is investigated here. Through construction of a solution process on Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and numerical examples with sensitive analysis, the validity of the model has been proved.     

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L₂ norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕ_p), stopband error (ϕ_s), transition band error (ϕ_t), peak reconstruction error (PRE), stopband attenuation (A_s) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

Generating synthetic benchmark circuits for accelerated life testing of field programmable gate arrays using genetic algorithm and particle swarm optimization

Accelerated life testing (ALT) of a field programmable gate array (FPGA) requires it to be configured with a circuit that satisfies multiple criteria. Hand-crafting such a circuit is a herculean task as many components of the criteria are orthogonal to each other demanding a complex multivariate optimization. This paper presents an evolutionary algorithm aided by particle swarm optimization methodology to generate synthetic benchmark circuits (SBC) that can be used for ALT of FPGAs. The proposed algorithm was used to generate a SBC for ALT of a commercial FPGA. The generated SBC when compared with a hand-crafted one, demonstrated to be more suitable for ALT, measured in terms of meeting the multiple criteria. The SBC generated by the proposed technique utilizes 8.37% more resources; operates at a maximum frequency which is 40% higher; and has 7.75% higher switching activity than the hand-crafted one reported in the literature. The hand-crafted circuit is very specific to the particular device of that family of FPGAs, whereas the proposed algorithm is device-independent. In addition, it took several man months to hand-craft the SBC, whereas the proposed algorithm took less than half-a-day.     

Modeling and parameter optimization for the design of vibrating screens

In this paper, we simulated the complex particle flow-behavior and screening efficiency on a linear vibrating screen using the Discrete Element Method (DEM). The simulations were validated with data from an adjustable experimental prototype screen. Then the novel application of non-linear regression modeling based on Support Vector Machines (SVMs) is used for mapping the sample space of operating parameters and vibrating screen configuration. Lastly, parameter optimization is implemented using Particle Swarm Optimization (PSO) algorithm. The primary findings proved that the SVM-based nonparametric model is not only feasible, but also highly adaptive to the parameter optimization that requires large-scale iterative computation. The non-parametric model established using the integration of DEM and SVM, combined with PSO algorithm in subsequent parameter optimization offered insights to the design and manufacture of vibrating screens.