Gaussian Particle Swarm Optimization Research Articles

A novel displacement back analysis method considering the displacement loss for underground rock mass engineering

Most back analysis methods for geotechnical engineering are based on the measured displacement. However, before the monitoring sections are assembled, the displacement—termed the displacement loss—has already been induced; this displacement is difficult to determine, and thus, it is not considered in the back analysis. In the present study, a novel displacement back analysis method considering the displacement loss is developed, that can obtain not only the reasonable mechanical parameters of rock masses but also the displacement loss. To reduce the computational cost of back analysis, a new hybrid optimization algorithm based on the Gaussian process (GP) and particle swarm optimization (PSO) is presented. The GP is used as an inexpensive fitness evaluation surrogate to predict the global optimum solution and accelerate the local search of PSO, which is employed to determine the best mechanical parameters for the model. Combined with FLAC3D numerical analysis, a novel back analysis method called GP-PSO-FLAC3D is proposed. The results of a case study demonstrate that this method can effectively predict more reasonable mechanical parameters and displacement loss using the monitored displacement. An engineering application in the auxiliary tunnel of the Jinping II hydropower station indicates that the elastic deformation of the surrounding rock increases rapidly after excavation, especially for deep tunnels, thereby resulting in a large displacement loss. The back analysis results for the main powerhouse of the Taian pumped storage power station indicate that the displacement loss also exists in engineering processes involving ordinary geostress conditions. Therefore, the displacement loss of a surrounding rock mass cannot be ignored in the stability evaluation or back analysis of underground engineering, especially for deep underground rock engineering.

Mutation based self regulating and self perception particle swarm optimization for efficient object tracking in a video

Object tracking is a pre-eminent task in many computer vision application.This paper addresses a realistic object tracking problem, considering single as well as multiple objects with different challenging perspectives. In view of this, a Mutation-based Self Regulating and self-Perception Particle Swarm Optimization (MSRSP-PSO) algorithm is proposed in order to explore the effective sampling strategy as well as to estimate and localize the optimum position of the target object. In conjunction with the optimization technique, an effective appearance model is also used for the reinforcement of tracker to achieve better accuracy. The appearance model is constructed with the joint distribution of the Local Binary Pattern (LBP)/Local Contrast Measure (LCM) based texture description and the Ohta color features. In pursuance of validating the efficacy of the proposed tracking algorithm, performance of other state-of-the-art methods such as, Particle Swarm Optimization (PSO) based tracking, Sequential Particle Swarm Optimization (SPSO) based tracking, and Adaptive Gaussian Particle Swarm Optimization (AGPSO) based tracking algorithms are compared with the proposed tracking algorithm. Both the quantitative and qualitative evaluation of the experiments, validate the effectiveness of the proposed technique.The performance measures include the convergence rate, center location error and the coverage test (PASCAL SCORE). Furthermore, the non-parametric statistical analysis using the Sign test and Wilcoxon signed rank test, is also figured out to validate the statistical superiority of the proposed MSRSP-PSO algorithm over others. The experimental results reveal that the proposed algorithm robustly track single as well as multiple similar objects with complex interaction.

Tracking a hand in interaction with an object based on single depth images

Tracking a hand in interaction with an object based on vision is a challenging research topic. The occlusions that occur during the hand-object interaction make it difficult to develop an effective tracking system. To overcome the impacts of occlusions, we build 3D models for both the hand and the manipulated object and propose a model-based tracking method to track the hand and the object simultaneously from single depth images during the hand-object interaction. The most likely hand-object state is searched by an improved particle filtering (PF) tracking algorithm in the high-dimensional hand-object space, which uses Gaussian particle swarm optimization (Gaussian PSO) algorithm to improve the process of particle sampling, moving the particles to the regions with higher likelihood. According to the proposed tracking algorithm, two kinds of hand-object tracking prototype systems are developed by using the graphics rendering engine OSG and off-screen rendering techniques. Experimental results demonstrate that the proposed method can track hand-object motion robustly with few particles.

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

Minimum Synchronous Inertia Requirement of Renewable Power Systems

The installed capacity of wind generation and photovoltaics (PV) in many countries is going to dominate generation fleets in a bid to meet growing renewable energy targets. Synchronous inertia has never been problematic as there was more available than needed, but it is being significantly reduced due to the increasing integration of nonsynchronous renewable generation. When the low bidding priced generation of wind and PV becomes considerably large, conventional economic dispatch algorithms can result in less online synchronous inertia and put power system security at risk. However, the compromise of power system security due to synchronous inertia shortage is not well studied in the literature. This paper develops a synchronous inertia constrained economic dispatch algorithm to satisfy the minimum required synchronous inertia of frequency control. Synchronous condensers and wind reserve are economically allocated to alleviate any shortage of synchronous inertia and frequency control ancillary services (FCAS). A Gaussian particle swarm optimization algorithm is introduced to simultaneously co-optimize the dispatch of synchronous generators and their FCAS, wind reserve, and synchronous condensers.

Three-dimensional wind turbine positioning using Gaussian particle swarm optimization with differential evolution

This paper studies the optimization of wind turbine positioning with multiple hub heights on flat terrain using the Gaussian Particle Swarm Optimization (GPSO) with differential evolution. The hub height of each turbine within the wind farm is added to the optimization variables, and the horizontal coordinates and the hub heights of all the turbines are optimized simultaneously. The objective is to minimize the ratio of cost and power product. Three typical wind cases are employed to test the effectiveness of the present method. Numerical results reveal the necessity of the three-dimensional (3D) optimization. By comparing the optimized solutions with the ones by the greedy algorithm and the genetic algorithm, it is concluded that the present method is able to produce optimized solutions with lower cost per product and higher power output in most circumstances, especially in complicated situations.

COMPARISON OF HYBRID METHODS WITH DIFFERENT META MODEL USED IN BRIDGE MODEL-UPDATING

Two hybrid model updating methods by integration of Gaussian mutation particle swarm optimization method, Latin Hypercube Sampling technique and meta models of Kriging and Back-Propagation Neural Network respectively were proposed, and the methods make the convergence speed of the model updating process faster and the Finite Element Model more adequate. Through the application of the hybrid methods to model updating process of a self-anchored suspension bridge in-service with extra-width, which showed great necessity considering the ambient vibration test results, the comparison of the two proposed methods was made. The results indicate that frequency differences between test and modified model were narrowed compared to results between test and original model after model updating using both methods as all the values are less than 6%, which is 25%−40% initially. Furthermore, the Model Assurance Criteria increase a little illustrating that more agreeable mode shapes are obtained as all of the Model Assurance Criteria are over 0.86. The particular advancements indicate that a relatively more adequate Finite Element Model is yielded with high efficiency without losing accuracy by both methods. However, the comparison among the two hybrid methods shows that the one with Back-Propagation Neural Network meta model is better than the one with Kriging meta model as the frequency differences of the former are mostly under 5%, but the latter ones are not. Furthermore, the former has higher efficiency than the other as the convergence speed of the former is faster. Thus, the hybrid method, within Gaussian mutation particle swarm optimization method and Back-Propagation Neural Network meta model, is more suitable for model updating of engineering applications with large-scale, multi-dimensional parameter structures involving implicit performance functions.

Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power

This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm.

FE Model Updating on an In-Service Self-Anchored Suspension Bridge with Extra-Width Using Hybrid Method

Nowadays, many more bridges with extra-width have been needed for vehicle throughput. In order to obtain a precise finite element (FE) model of those complex bridge structures, the practical hybrid updating method by integration of Gaussian mutation particle swarm optimization (GMPSO), Kriging meta-model and Latin hypercube sampling (LHS) was proposed. By demonstrating the efficiency and accuracy of the hybrid method through the model updating of a damaged simply supported beam, the proposed method was applied to the model updating of a self-anchored suspension bridge with extra-width which showed great necessity considering the results of ambient vibration test. The results of bridge model updating showed that both of the mode frequencies and shapes had relatively high agreement between the updated model and experimental structure. The successful model updating of this bridge fills in the blanks of model updating of a complex self-anchored suspension bridge. Moreover, the updating process enables other model updating issues for complex bridge structures

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

Improved Euclidean Particle Swarm and Application

Particle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to many engineering optimization problems and shown its high search speed in these applications. However, as the dimension and the number of local optima of optimization problems increase, PSO and most existing improved PSO algorithms such as, the standard particle swarm optimization (SPSO) and the Gaussian particle swarm optimization (GPSO), are easily trapped in local optima. In this paper we proposed a novel algorithm based on SPSO called Euclidean particle swarm optimization (EPSO) which has greatly improved the ability of escaping from local optima. To confirm the effectiveness of EPSO, we have employed five benchmark functions to examine it, and compared it with SPSO and GPSO. The experiments results showed that EPSO is significantly better than SPSO and GPSO, especially obvious in higher-dimension problems. As one of tis application, we applied EPSO to the structure prediction of toy model both on artificial and real protein sequences. Predicting the structure of protein through its sequence of amino acids is a complex and challenging problem in computational biology. Though toy model is one of the simplest and effective models, it is still extremely difficult to predict its structure as the increase of amino acids. The experimental results demonstrated that EPSO was efficient in protein structure prediction problem in toy model.

Optimisasi Economic Dispatch dengan Transmission Loss Menggunakan Metode Extended Lagrange Multiplier dan Gaussian Particle Swarm Optimization (Gpso)

In the operating process of electrical energy, economic planning is the main goal to be achieved. The goal of economic dispatch problem is determining the combination of optimal power distribution to a number of operating generator units so that the electricity demand in a certain area is fulfilled without ignoring the constraints that exist, so it is obtained a minimum total generation cost. Optimization method that is used is the Gaussian Particle Swarm Optimization (GPSO), while for the validation of the results, the obtained solution with the GPSO will be compared with the solution obtained by mathematical methods Extended Lagrange Multiplier (ELM) or the Lagrange multiplier method which its functions are expanded. The solution that is calculated is generation output in Megawatt of 23 thermal generating units system in Mahakam, East Kalimantan, which had a total cost of optimal generating (minimum).

Computational methods of Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier on economic dispatch issues (case study on electrical system of Java-Bali IV area)

The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method.

APPLICATION OF PARTICLE SWARM OPTIMIZATION FOR ENHANCED CYCLIC STEAM STIMULATION IN A OFFSHORE HEAVY OIL RESERVOIR

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and L\'evy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able to improve the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The L\'evy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigated in this study, it is found that the temperature of the injection steam, CO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate have less impact on the objective function.

Application of Particle Swarm Optimization for Enhanced Cyclic Steam Stimulation in a Offshore Heavy Oil Reservoir

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and Levy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able toimprove the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The Levy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigatedin this study, it is found that the temperature of the injection sCO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate ha ve less impact on the objective function.

The Study and Application of Gaussian Process Surrogate Model Based on Gradient Particle Swarm Optimized Hyper-Parameters

Constructing a high-fidelity surrogate model to optimize production process is often required to meet the requirement of manufacturing process programming ,one of the most popular techniques for the construction of such a surrogate model is that of Gaussian process surrogate model. In this paper, the development of a gradient particle swarm optimization is described, which aims to reduce the cost of this likelihood optimization by drawing on an efficient adjoint of the likelihood, and improve the precision of the model. A multimodal benchmark function was used to test, show that the tuning strategy can provide an accurate Gaussian process surrogate model. Based on LHS ,Gaussian process surrogate model (GP) and gradient particle swarm optimization algorithm (GPSO), a optimization model which is used for improving the quality of Al profile welding is built and utilized to obtain optimal multi-parameters of Al alloy profile extruding processes and moulds. Optimal solution is validated by experiment.

Erratum to: Non-parametric identification of continuous-time Hammerstein systems using Gaussian process model and particle swarm optimization

This paper deals with a non-parametric identification of continuous-time Hammerstein systems using Gaussian process (GP) models. A Hammerstein system consists of a memoryless non-linear static part followed by a linear dynamic part. The identification model is derived using the GP prior model which is described by the mean function vector and the covariance matrix. This prior model is trained by the separable least-squares (LS) approach combining the linear LS method with particle swarm optimization to minimize the negative log marginal likelihood of the identification data. Then the non-linear static part is estimated by the predictive mean function of the GP, and the confidence measure of the estimated non-linear static part is evaluated by the predictive covariance function of the GP. Simulation results are shown to illustrate the proposed method.

Non-parametric identification of continuous-time Hammerstein systems using Gaussian process model and particle swarm optimization

Non-parametric identification of continuous-time Hammerstein systems using Gaussian process model and particle swarm optimization

Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy

The micro-siting of wind farms has recently attracted much attention due to the booming development of wind energy. The paper aims to maximize the electrical power extracted from a wind farm while satisfying the required distance between turbines for operation safety. The micro-siting problem is by nature a constrained optimization problem, in which the coupling of wake effects is strong and the number of position constraints between turbines is large. An improved Gaussian particle swarm optimization algorithm is proposed to optimize the positions of turbines in the continuous space. To prevent the premature of the algorithm, a local search strategy based on differential evolution is incorporated to search around the promising region achieved by the particle swarm optimization. A simple feasibility-based method is employed to compare the performance of different schemes. Comprehensive simulation results demonstrate that the micro-siting schemes obtained by the proposed algorithm increase the power generation of the wind farm. Moreover, the execution time of the algorithm is significantly reduced, which is important especially for large-scale wind farms.

Perbandingan Metode Gaussian Particle Swarm Optimization (GPSO) dan Lagrange Multiplier pada Masalah Economic Dispatch

On electric power system operation, economic planning problem is one variable to take into account due to operational cost efficiency. Economic Dispatch problem of electric power generation is discussed in this study to manage the output division on several units based on the the required load demand, with minimum operating cost yet is able to satisfy equality and inequality constraint of all units and system. In this study the Economic Dispatch problem which has non linear cost function is solved using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving is inspired by swarm intelligent and probabilities theories. To analize its accuracy, the Economic Dispatch solution by GPSO method is compared with Lagrange Multiplier method. From the test result it is proved that GPSO method gives economic planning calculation better than Lagrange Multiplier does.