Dynamic Multi-swarm Particle Swarm Optimizer Research Articles

Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination

BackgroundIntegration of a patient’s non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019–10–07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013–11–12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient (ICC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${ICC}$$\\end{document}) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error (χ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\chi}}}^{{2}}$$\\end{document}) of LV myocardial strain, strain rate, and cavity volume.ResultsA subset of 270 parameters remained after MSM which produced high-quality DTs of all patients (χ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\chi}}}^{{2}}$$\\end{document} < 1.6), but minimum parameter reproducibility was poor (ICCmin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{ICC}}_{{min}}$$\\end{document} = 0.01). Iterative reduction yielded a reproducible (ICCmin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{ICC}}_{{min}}$$\\end{document} = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs (χ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\chi}}}^{{2}}$$\\end{document} < 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p < 0.05).ConclusionsBy applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work distribution was representative for the patient’s underlying disease substrate. This DT technology enables patient-specific substrate characterization and can potentially be used to support clinical decision making.

Uncovering personal circadian responses to light through particle swarm optimization

Background and objectivesKronauer's oscillator model of the human central pacemaker is one of the most commonly used approaches to study the human circadian response to light. Two sources of error when applying it to a personal light exposure have been identified: (1) as a populational model, it does not consider inter-individual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined Kronauer's oscillator model. MethodsA Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of the model. ResultsWe demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected, optimization is easier under a regular schedule. Circadian parameters play the most important role in the optimization process and should be prioritized over initial conditions, although assessment of the impact of misestimating the latter is recommended. The log-log linear relationship between mean absolute error and computational cost shows that the number of particles to use is at the discretion of the user. ConclusionsThe robustness and low errors achieved by the algorithms support their further testing, validation and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian parameters can improve the assessment of the circadian status of a person and the applicability of personalized light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the circadian response to light.

A Hybrid Firefly with Dynamic Multi-swarm Particle Swarm Optimization for WSN Deployment

&lt;p&gt;Enhancing the coverage area of the sensing range with the limiting resource is a critical problem in the wireless sensor network (WSN). Mobile sensors are patched coverage holes and they also have limited energy to move in large distances. Several recent studies indicated the metaheuristic algorithms can find an acceptable deployed solution in a reasonable time, especially the PSO-based algorithm. However, the speeds of convergence of most PSO-based algorithms are too fast which will lead to the premature problem to degrade the quality of deployed performance in WSN. A hybrid metaheuristic combined with dynamic multi-swarm particle swarm optimization and firefly algorithm will be presented in this paper to find an acceptable deployed solution with the maximum coverage rate and minimum energy consumption via static and mobile sensors. Moreover, a novel switch search mechanism between sub-swarms will also be presented for the proposed algorithm to avoid fall into local optimal in early convergence process. The simulation results show that the proposed method can obtain better solutions than other PSO-based deployment algorithms compared in this paper in terms of coverage rate and energy consumption.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

An adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion for solving continuous optimization problems

Particle swarm optimization (PSO) is a simple and efficient optimization method that has been used in many fields. However, PSO has some flaws including premature convergence and poor population diversity. To solve these problems of PSO, an adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion (ADPSO) is proposed. The newly proposed ADPSO is based on a dynamic multi-swarm PSO framework that cooperates with stagnation detection mechanism (SDM) and spatial exclusion strategy (SES). Firstly, the whole population is divided into multiple equal sub-swarms, which can be regrouped during evolution. The best particle of each sub-swarm, lbest, is used to evaluate the evolutionary state of each sub-swarm. If the lbest cannot improve its solution continuously without reaching the regroup period, the SDM will be triggered. In this case, a vitality particle is generated to help the stagnant sub-swarm to search for promising areas again. To keep the population diversity, the vitality particle is constructed according to the excellent historical information of all particles in the whole population. Secondly, to enhance the diversity of the population, the SES is proposed to avoid premature adsorption of all sub-swarms. Finally, the effect of ADPSO is evaluated using CEC2013, CEC2017 and four engineering optimization problems. The results shows that the proposed ADPSO is suitable for solving most optimization problems, and ADPSO has better results compared to the state-of-the-art PSO variants (i.e., MSCPSO, HCDMPS) and other popular evolutionary algorithms.

Cooperative path planning optimization for multiple UAVs with communication constraints

Path planning is a complicated optimization problem that is crucial for the safe flight of unmanned aerial vehicles (UAVs). Especially in the scenarios involving multiple UAVs, this problem is highly challenging due to the constraints of complex environments, various tasks and inherent UAV maneuverability. In this paper, a cooperative path planning model for multiple UAVs is presented. In addition to common limitations such as the path length minimization, UAV maneuverability limitation and collision avoidance, the communication requirements between UAVs and the impact of obstacles in the flight environment on the quality of communication are also taken into account in the presented path planning model. On this basis, the corresponding objective function is designed. Then, an improved particle swarm optimization (PSO) algorithm is proposed to solve the above path planning problem. Utilizing the ideas of the dynamic multi-swarm PSO (DMSPSO) algorithm and the comprehensive learning PSO (CLPSO) algorithm, the proposed algorithm, denoted as CL-DMSPSO, further improves the performance of both algorithms. The effectiveness and superiority of the novel CL-DMSPSO algorithm is verified on benchmark functions, especially for complex multimodal functions. Finally, we present an effective path planning method using CL-DMSPSO to generate optimized flyable paths for multiple UAVs. And simulation and comparison results on the designed scenario indicate the proposed UAV path planning method can efficiently plan high-quality paths for UAVs and demonstrate the advantages of the proposed CL-DMSPSO algorithm compared with other PSO algorithms in UAV path planning.

A Dynamic Multi-Swarm Particle Swarm Optimization With Global Detection Mechanism

To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.

Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization.

One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm's efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods' poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm's global search capability. The fundamental concept is to combine GSA's ability with DMSPSO's to improve the performance of a given algorithm's exploration and exploitation. The suggested algorithm's performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.

Prediction of GPS Satellite Clock Offset Based on an Improved Particle Swarm Algorithm Optimized BP Neural Network

Satellite clock offset is an important factor affecting the accuracy of real-time precise point positioning (RT-PPP). Due to missing real-time service (RTS) products provided by the International GNSS Service (IGS) or network faults, users may not obtain effective real-time corrections, resulting in the unavailability of RT-PPP. Considering this issue, an improved back propagation (BP) neural network optimized by heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer (HPSO-BP) is proposed for clock offset prediction. The new model uses the particle swarm optimizer to optimize the initial parameters of the BP neural network, which can avoid the instability and over-fitting problems of the traditional BP neural network. IGS RTS product data is selected for the experimental analysis; the results demonstrate that the average prediction precision of the HPSO-BP model for 20-min and 60-min is better than 0.15 ns, improving by approximately 85% compared to traditional models including the linear polynomial (LP) model, the quadratic polynomial (QP) model, the gray system model (GM (1,1)), and the ARMA time series model. It indicates that the HPSO-BP model has reasonable practicability and stability in the short-term satellite clock offset prediction, and its prediction performance is superior to traditional models. Therefore, in practical applications, the clock offset products predicted by the HPSO-BP model can meet the centimeter-level positioning accuracy requirements of RT-PPP.

Fatigue properties and S-N curve estimating of 316L stainless steel prepared by SLM

This paper investigates the fatigue properties of 316L stainless steel manufactured by selective laser melting technology (SLM) under hot-isostatic-pressing (HIP) heat-treatment. The fatigue tests of 316L specimens were carried out under pulse tensile cyclic loading to obtain the fatigue performance. To avoid falling into local optima and improve the convergence speed, the dynamic multiswarm particle swarm optimizer (DMS-PSO) algorithm was first introduced for parameter optimization of the three-parameter Weibull distribution model, and then the S-N curves based on the model were obtained. The S-N curves were highly consistent with the metallographic and fractographic phenomena. Besides, both the S-N curves and fractographic analysis show that the resistance of high-stress fatigue fracture is significantly improved by using HIP heat-treatment.

Fitness peak clustering based dynamic multi-swarm particle swarm optimization with enhanced learning strategy

Particle Swarm Optimization (PSO) is a well-known swarm intelligence algorithm and its performance primarily depends on the tradeoff between exploration and exploitation. In order to well balance the exploration and exploitation, this paper presents a fitness peak clustering based dynamic multi-swarm Particle Swarm Optimization (FPCMSPSO) with enhanced learning strategy. In the presented FPCMSPSO, first, FPC-based partitioning method is utilized to divide the initialized population into several sub-swarms so as to avoid crossover evolution caused by random partitioning. These sub-swarms evolve independently based on comprehensive learning strategy and along with further evolution they would merge into a global swarm according to their own stagnancy information. Second, an enhanced learning strategy is exploited to some particles, and their velocities are updated based on learning exemplars alternately generated by comprehensive learning or dimensional learning strategies according to their stagnancy information. Extensive experimental results demonstrate that the solution accuracy, convergence speed and stability of FPCMSPSO are remarkably improved due to the usage of above strategies. The comparative results of FPCMSPSO with other existing PSO variants on various optimization problems show that FPCMSPSO statistically outperforms other PSO variants with significant difference.

Employing reinforcement learning to enhance particle swarm optimization methods

Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this article, a reinforcement learning strategy is developed to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function with a random number generated from a selected normal distribution. In the proposed method, the mean and standard deviation of the normal distribution are estimated from the current state of each individual through a policy net. The historic behaviour of the swarm group is used to update the policy net and guide the selection of parameters of the normal distribution. The proposed method is integrated into the original PSO and a state-of-the-art PSO, called the self-adaptive dynamic multi-swarm PSO (sDMS-PSO), and tested with numerical functions and engineering problems. The test results show that the convergence rate of PSO methods can be improved with the proposed reinforcement learning strategy.

Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators

In this paper, a heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (HCLDMS-PSO) is presented. In addition, a comprehensive learning (CL) strategy with the global optimal experience of the whole population is conducted to generate an exploitation subpopulation exemplar. However, a modified dynamic multi-swarm (DMS) strategy is specially designed to construct the exploration subpopulation exemplar. In the canonical DMS strategy, it is unfavorable for different sub-swarms to use the same linear decreasing inertia weight parameter. We first propose classifying the DMS sub-swarms at the search level and then constructing a novel nonlinear adaptive decreasing inertia weight for different sub-swarms, introducing a non-uniform mutation operator to enhance its exploration capability. Finally, the gbest of the whole population also adopts a Gaussian mutation operator to avoid falling into the local optimum. The particles of the two subpopulations will update their velocity independently without crippling one another to prevent a loss of diversity. The performance of HCLDMS-PSO is compared with those of 8 other PSO variants and 11 evolutionary algorithms on two classical benchmark optimization problems and a real-world engineering problem. Experimental results demonstrate that the HCLDMS-PSO improves the convergence speed, accuracy, and reliability on most optimization problems.

A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption

Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

Novel chaotic grouping particle swarm optimization with a dynamic regrouping strategy for solving numerical optimization tasks

Particle swarm optimization (PSO) has been widely applied to address various optimization problems, since it is easy to implement and only has a few control parameters. However, PSO often suffers from a lack of population diversity during the search process and is ineffective in balancing exploration and exploitation, especially in solving complex numerical optimization tasks. To overcome these disadvantages of PSO for complex numerical optimization problems, a new chaotic grouping PSO algorithm with a dynamic regrouping strategy (CGPSO-DRS) is proposed in this paper. The newly proposed CGPSO-DRS is based on a dynamic multiswarm PSO framework that cooperates with the chaotic grouping mechanism (CGM) and the dynamic regrouping strategy (DRS). First, the CGM divides the entire population into many subswarms via a chaotic sequence. The CGM not only improves the population grouping quality in the search process but also increases the diversity of the population. Second, the DRS is used to guide the regrouping of the population, and the population starts searching with a new configuration. Here, the DRS changes with the number of function evaluations. The DRS facilitates the effective utilization of information to balance the early exploration and the later exploitation performances. In addition, the DRS can increase the diversity of the population in the search process. Experiments have been conducted on 41 benchmark functions, and the numerical results demonstrate that the proposed CGPSO-DRS method outperforms similar population-based approaches and state-of-the-art PSO variants in accelerating the convergence speed and finding the global optimum.

Dynamic Multi-Swarm Particle Swarm Optimization Based on Elite Learning

This paper presents a dynamic multi-swarm particle swarm optimization based on an elite learning strategy (DMS-PSO-EL). In DMS-PSO-EL, the whole evolutionary process is divided into a former stage and a later stage. The former and later stages are focus on the exploration and the exploitation, respectively. In the former stage, the entire population is divided into multiple dynamic sub-swarms and a following sub-swarm according to the particles’ fitness values. In each generation, the dynamic sub-swarms evolve independently, which is beneficial for keeping population diversity, while particles in the following sub-swarm choose elites in the dynamic sub-swarms as their learning exemplars aiming to find out more promising solutions. To take full advantages of the different sub-swarms and then speed up the convergence, a randomly dynamic regrouping schedule is conducted on the entire population in each regrouping period. In the latter stage, all the particles select the historical best solution of the entire population as an exemplar aiming to enhance the exploitation ability. The comparison results among DMS-PSO-EL and other 9 well-known algorithms on CEC2013 and CEC2017 test suites suggest that DMS-PSO-EL demonstrates superior performance for solving different types of functions. Furthermore, the sensitivity and performance of the proposed strategies in DMS-PSO-EL are also testified by a set of experiments.

Application of model-based control strategy to hybrid free cooling system with latent heat thermal energy storage for TBSs

This paper explored the application of model predictive control (MPC) technology to the TBSs hybrid free cooling system with latent heat thermal energy storage (LHTES) unit for minimizing the building operational cost without sacrificing temperature requirements. First, the system was briefly introduced and the dynamic thermal process models of building structure and LHTES unit were developed. Then, a hierarchical control structure with dynamic multi-swarm particle swarm optimization was presented to address the dimensional challenge and discontinuities in control variables. Due to the considerable decrease of optimization variable space, the method presented in this paper enables long-term simulation and application in a real controller. Simulations were carried out based on a typical TBS building located in Beijing, China. The total energy consumption of the cooling system and the control quality of indoor air temperature were used as the criteria to evaluate the performance. Compared to a defined baseline case, the optimal control method can achieve significant energy saving, i.e. up to 18%. The impacts of the size of LHTES unit and the type of building structure were discussed, as well. The active and passive heat capacity both played a catalytic role in performance of MPC. Additionally, an uncertainty analysis demonstrated that the proposed approach has strong robustness and can handle quite high errors in forecasting building disturbances from energy consumption level. In summary, the knowledge and use of the plant system and future disturbances make MPC a powerful control tool for TBS buildings for maximizing the use of renewable energy sources.

Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease

Expert’s knowledge base systems are not effective as a decision-making aid for physicians in providing accurate diagnosis and treatment of heart diseases due to vagueness in information and impreciseness and uncertainty in decision making. For this reason, automatic diagnostic fuzzy systems are very time demanding to improve the diagnostic accuracy. In this paper, we have developed an automatic fuzzy diagnostic system based on genetic algorithm (GA) and a modified dynamic multi-swarm particle swarm optimization (MDMS-PSO) for prognosticating the risk level of heart disease. Our proposed fuzzy diagnostic system (FS) works as follows: i) Preprocess the data sets ii) Effective attributes are selected through statistical methods such as Correlation coefficient, R-Squared and Weighted Least Squared (WLS) method, iii) Weighted fuzzy rules are formed on the basis of selected attributes using GA, iv) MDMS-PSO is employed for the optimization of membership functions (MFs) of FS, v) Build the ensemble FS from the generated fuzzy knowledge base by fusing the different local FSs. Finally, to ascertain the efficiency of the adaptive FS, the applicability of the FS is appraised with quantitative, qualitative and comparative analysis on the publicly available different real-life data sets. From the empirical analysis, we see that this hybrid model can manage the knowledge vagueness and decision-making uncertainty precisely and it has yielded better accuracy on the different publicly available heart disease data sets than other existing methods so that it justifies its adaptability with different data sets.

Interrogation of a spectral profile division multiplexed FBG sensor network using a modified particle swarm optimization method

This paper applies the concept of spectral profile division multiplexing to track each Bragg wavelength shift in a serially multiplexed fiber Bragg grating (FBG) network. Each sensor in the network is uniquely characterized by its own reflected spectrum shape, thus spectral overlapping is allowed in the wavelength domain. In contrast to the previous literature, spectral distortion caused by multiple reflections and spectral shadowing between FBG sensors, that occur in serial topology sensor networks, are considered in the identification algorithm. To detect the Bragg wavelength shift of each FBG, a nonlinear optimization function based on the output spectrum is constructed and a modified dynamic multi-swarm particle swarm optimizer is employed. The multiplexing approach is experimentally demonstrated on data from multiplexed sensor networks with up to four sensors. The wavelength prediction results show that the method can efficiently interrogate the multiplexed network in these overlapped situations. Specifically, the maximum error in a fully overlapped situation in the specific four sensor network demonstrated here was only 110 pm. A more general analysis of the prediction error and guidelines to optimize the sensor network are the subject of future work.

Dynamic Equivalent Modeling for Small and Medium Hydropower Generator Group Based on Measurements

At present, the common practice in the power system of China is to represent the small and medium hydropower generator group as a negative load. This paper presents a method to build a dynamic equivalent model of the hydropower generator group using a 3rd order generator model and a static characteristic load model. Based on phasor measurements in the tie line which connects to the modeled hydropower generator group, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) algorithm is used to obtain parameters of the equivalent model. The proposed method is verified in the small and medium hydropower generator group of Sichuan power grid with both simulation and actual data. The results show that the dynamic responses and the transient stability are consistent before and after the equivalence. The proposed method can be used for modeling a group of small and medium hydropower generators whose structures and parameters are unknown.

Dynamic Multi-Swarm-Particle Swarm Optimizer (DMS-PSO) for Time and Energy Efficient Cluster Head Selection in WSN

Dynamic Multi-Swarm-Particle Swarm Optimizer (DMS-PSO) for Time and Energy Efficient Cluster Head Selection in WSN