Global Particle Swarm Optimization Algorithm Research Articles

A multi-step calibration strategy for reliable parameter determination of salt rock mechanics constitutive models

The storage of renewable hydrogen in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. This raises concerns about the mechanical stability of salt caverns under such operational conditions. The use of appropriate constitutive models for salt mechanics is an important step in investigating this issue, therefore many constitutive models with several parameters have been presented in the literature. However, a robust calibration strategy to reliably determine which model and parameter set represents the given rock, based on stress–strain data sets, remains an unsolved challenge. In this paper, for the first time in the community, we present a multi-step strategy to determine a single parameter set based on many deformation data sets for salt rocks. Towards this end, we first develop a comprehensive constitutive model able to capture all relevant nonlinear deformation physics of transient, reverse, and steady-state creep. The determination of the single set of representative material parameters is then achieved by framing the calibration process as an optimization problem, for which the global Particle Swarm Optimization algorithm is employed. To allow for dynamic data integration, a multi-step calibration strategy is developed for a situation where experiments are included one at a time, as they become available. Additionally, due to the existing mild heterogeneity in the experimental rock samples, our optimization strategy is made flexible to allow for this slight heterogeneity. The devised optimization strategy, based on the multi-physics comprehensive constitutive modeling framework, results in a single set of representative material properties of all the deformation data sets. As a rigorous mathematical analysis for the presented method and the lack of relevant experimental data sets, we consider a wide range of synthetic experimental data sets, inspired by the existing sparse relevant data in the literature. The results of our performance analyses show that the proposed calibration strategy is robust. Moreover, the results become increasingly more accurate as more data sets become available.

Magnetic field analysis using the improved global particle swarm optimization algorithm to estimate the depth and approximate shape of the buried mass

In this paper, the optimization algorithm based on the population as improved global particle swarm optimization is described and used for inverse modelling of two-dimensional magnetic field data. This algorithm is able to estimate the parameters of depth, shape factor, amplitude coefficient, magnetic inclination angle and origin point coordinates. To evaluate the efficiency of this method, the magnetic field of an artificial model was analysed, with and without added random noise. The results suggest that the proposed algorithm is capable of model parameter estimation with high accuracy. Accordingly, the improved global particle swarm optimization algorithm was used to analyse the magnetic field of the study area in the Ileh region in Iran located in Taybad city. The study area is very rich in terms of iron resources. The estimate for the study area is that the depth of the buried mass centre is about 114.9 m and its approximate shape is similar to a horizontal cylinder based on the calculated shape factor value which is 1.76. The calculated depth is an acceptable match with the average depth of drillings.

Process Synthesis and Design Problems Based on a Global Particle Swarm Optimization Algorithm

Many process synthesis and design problems in engineering are actually mixed integer nonlinear programming problems (MINLP), because they contain both continuous and integer variables. These problems are generally recognized to be complex and intractable by virtue of the combinatorial characteristic. In order to effectively solve process synthesis and design problems, a global particle swarm optimization (GPSO) algorithm is proposed in this paper. GPSO algorithm makes two improvements on original particle swarm optimization (PSO) algorithm: first, it introduces a global inertia weight, which is beneficial for improving its global searching capacity during the whole optimization process; second, it adopts a mutation operation with a small probability, which enables the GPSO algorithm to get rid of the local optimum easily. Simulation results show that the GPSO algorithm has high efficiency on finding the optimal solutions, and it has stronger convergence than the other four particle swarm optimization algorithms.

Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities

Blueberry fruits of different cultivars are featured with different quality indices. In this work, three types of quality factors, including 6 physical parameters, 12 chemical and nutritional components, and 3 antioxidant indices, were measured to compare and classify blueberry fruits from 12 different cultivars in China. Using the autoscaled data of quality factors, unsupervised principal component analysis was performed for exploratory analysis of intercultivar differences and the influences of quality factors. A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. As a result, OVO-PLSDA with 8 quality factors could achieve the classification accuracy of 0.915. This study will provide new insights into the quality variations and key factors among different blueberry cultivars.

A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.

Si2Ge: A New VII-Type Clathrate with Ultralow Thermal Conductivity and High Thermoelectric Property

Based on global particle-swarm optimization algorithm and density functional theory methods, we predicted an alloyed Si2Ge compond with body centered tetragonal type VII clathrate (space group I4/mmm) built by a truncated octahedron fromed by six quadrangles and eight hexagons ([4668]). Si2Ge clathrate is 0.06 eV/atom lower than VII Si clathrate and thermally stable up to 1000 K. It has an indirect band gap of 0.23 eV, high p-doping Seebeck coefficient and n-doping electrical conductivity. It owns a low lattice thermal conductivity of 0.28 W/mK at 300 K because of its weak bonding and strong anharmonic interaction of longitudinal acoustic and low-lying optical phonons. The moderate electronic transport properties together with low lattice thermal conductivity results in a high optimal thermoeletric performance value of 2.54 (1.49) at 800 (1000) K in n (p)-doped Si2Ge.

Joint Pricing and Decision-Making for Heterogeneous User Demand in Cognitive Radio Networks.

The cognitive radio technique allows secondary users (SUs) to share the spectrum with primary users (PUs) in an exclusive or opportunistic manner. This paper studies spectrum pricing conducted by spectrum owners, that is, primary operators (POs), and SU decision-making strategies for three kinds of duopoly markets. The single-band exclusive use market considers two POs with each providing a single band dedicated to SUs. A pre-emptive resume priority (PRP) M/M/1 queueing model is presented, based on which SUs decide to join which PO and which queue. We prove the existence of a unique Wardrop equilibrium for the decision-making process, and a unique Nash equilibrium for the proposed parallel pricing strategy. In a single-band mixed use market, the competition of two POs is represented by a Stackelberg game. We formulate the spectrum sharing among PU and SUs with a 3-level PRP M/M/1 queueing structure, and derive the close form expressions of SUs' queueing delay. In a multiband exclusive use market, where POs have to determine how many bands they will rent as well as the admission price, we define the problem as a mixed integer linear programming problem and propose a global particle swarm optimization algorithm to find the global optimum. Finally, we study a generalized scenario with multiple POs and multiple priority queues.

A novel orthogonal PSO algorithm based on orthogonal diagonalization

Abstract One of the major drawbacks of the global particle swarm optimization (GPSO) algorithm is zigzagging of the direction of search that leads to premature convergence by falling into local minima. In this paper, a new algorithm named orthogonal PSO (OPSO) algorithm is proposed that not only alleviates the associated problems in GPSO algorithm but also achieves better performance. In OPSO algorithm, the m particles of the swarm are divided into two groups: one active group of best personal experience of d particles and a passive group of personal experience of remaining (m ‒ d) particles. The purpose of creating two groups is to enhance the diversity in the swarm's population. In each iteration, the d active group particles undergo an orthogonal diagonalization process and are updated in such way that their position vectors are orthogonally diagonalized. The passive group particles are not updated as their contribution in finding correct direction is not significant. In the proposed algorithm, the particles are updated using only one guide, thus avoiding the conflict between the two guides that occurs in the GPSO algorithm. We tested the OPSO algorithm with thirty unimodal and multimodal high-dimensional benchmark functions and compared its performance with GPSO and several competing evolutionary techniques. With extensive simulated experiments, we have shown superiority of the proposed algorithm in terms of convergence, accuracy, consistency, robustness and reliability over other algorithms. The proposed algorithm is found to be successful in achieving optimal solution in all the thirty benchmark functions.

Orthogonal PSO algorithm for economic dispatch of thermal generating units under various power constraints in smart power grid

Particle swarm optimization (PSO) algorithm has been successfully applied to solve various optimization problems in science and engineering. One such popular one is called global PSO (GPSO) algorithm. One of major drawback of GPSO algorithm is the phenomenon of “zigzagging”, that leads to premature convergence by falling into local minima. In addition, the performance of GPSO algorithm deteriorates for high-dimensional problems, especially in presence of nonlinear constraints. In this paper we propose a novel algorithm called, orthogonal PSO (OPSO) that alleviates the shortcomings of the GPSO algorithm. In OPSO algorithm, the m particles of the swarm are divided into two groups: active group and passive group. The d particles of the active group undergo an orthogonal diagonalization process and are updated in such way that their position vectors become orthogonally diagonalized. In the OPSO algorithm, the particles are updated using only one guide, thus avoiding the conflict between the two guides that occurs in the GPSO algorithm. We applied the OPSO algorithm for solving economic dispatch (ED) problem by taking three power systems under several power constraints imposed by thermal generating units (TGUs) and smart power grid (SPG), for example, ramp rate limits, and prohibited operating zones. In addition, the OPSO algorithm is also applied for ten selected shifted and rotated CEC 2015 benchmark functions. With extensive simulation studies, we have shown superior performance of OPSO algorithm over GPSO algorithm and several existing evolutional computation techniques in terms of several performance measures, e.g., minimum cost, convergence rate, consistency, and stability. In addition, using unpaired t-Test, we have shown the statistical significance of the OPSO algorithm against several contending algorithms including top-ranked CEC 2015 algorithms.

A global particle swarm optimization algorithm applied to electromagnetic design problem

A global particle swarm optimization algorithm applied to electromagnetic design problem

Application of Global Particle Swarm Optimization for Inversion of Residual Gravity Anomalies Over Geological Bodies with Idealized Geometries

A global particle swarm optimization (GPSO) technique is developed and applied to the inversion of residual gravity anomalies caused by buried bodies with simple geometry (spheres, horizontal, and vertical cylinders). Inversion parameters, such as density contrast of geometries, radius of body, depth of body, location of anomaly, and shape factor, were optimized. The GPSO algorithm was tested on noise-free synthetic data, synthetic data with 10% Gaussian noise, and five field examples from different parts of the world. The present study shows that the GPSO method is able to determine all the model parameters accurately even when shape factor is allowed to change in the optimization problem. However, the shape was fixed a priori in order to obtain the most consistent appraisal of various model parameters. For synthetic data without noise or with 10% Gaussian noise, estimates of different parameters were very close to the actual model parameters. For the field examples, the inversion results showed excellent agreement with results from previous studies that used other inverse techniques. The computation time for the GPSO procedure is very short (less than 1 s) for a swarm size of less than 50. The advantage of the GPSO method is that it is extremely fast and does not require assumptions about the shape of the source of the residual gravity anomaly.

A Self-adaptive Global Particle Swarm Optimization Algorithm for Unconstrained Optimization Problems

This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.

Responsive Theater Maneuvers via Particle Swarm Optimization

This research investigates the performance of the particle swarm optimization algorithm in the solution of responsive theater maneuvers, introduced here for the first time. The responsive theater maneuver is designed to alter a spacecraft’s arrival position as it overflies a hazardous geographic region, while still meeting sensor range constraints. The maneuver places the satellite on an exclusion ellipse centered at the spacecraft’s expected arrival position at the expected time of entry into the hazardous region. A global particle swarm optimization algorithm is shown to generate optimal solutions for the single-pass responsive theater maneuver scenario in shorter time frames than local particle swarm variants, a genetic algorithm, and a parameter search. The global particle swarm algorithm is then shown to generate consistent performance in the solution of single-, double-, and triple-pass responsive theater maneuver scenarios for various size exclusion ellipses.

Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

SiC2Siligraphene and Nanotubes: Novel Donor Materials in Excitonic Solar Cells

In excitonic solar cells (XSC), power conversion efficiency (PCE) depends critically on the interface band alignment between donor and acceptor materials. Graphene or silicene is not suitable for donor materials due to their semimetallic features (zero band gaps); it is therefore highly desired to open an energy gap in graphene or silicene to extend their application in optoelectronic devices, especially in photovoltaics. In this paper, based on the global particle-swarm optimization algorithm and the density functional theory methods, we predict a novel SiC2 siligraphene (g-SiC2) with a direct band gap of 1.09 eV showing infinite planar geometry, in which Si and C atoms adopt sp(2) hybridization and C atoms form delocalized 4 C-domains that are periodically separated by Si atoms. Such a g-SiC2 siligraphene (with a global minimum of energy) is 0.41 eV/atom lower and thermally stabler than the isomeric pt-SiC2 silagraphene containing planar 4-fold coordinated silicon (3000 K vs 1000 K). Interestingly, the derivative (n, 0), (n, n) nanotubes (with diameters greater than 8.0 Å) have band gaps about 1.09 eV, which are independent of the chirality and diameter. Besides, a series of g-SiC2/GaN bilayer and g-SiC2 nanotube/ZnO monolayer XSCs have been proposed, which exhibit considerably high PCEs in the range of 12-20%.

Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

The investigation of the performance of the Particle Swarm Optimization (PSO) method for Vehicle Routing Problem with Time Windows is the main theme of the paper. “Exchange minus operator” is constructed to compute particle’s velocity. We use Saving algorithm, Nearest Neighbor algorithm, and Solomon insertion heuristics for parameter initialization and apply the “Routing first and Cluster second” strategy for solution generation. By PSO, customers are sorted in an ordered sequence for vehicle assignment and Nearest Neighbor algorithm is used to optimize every vehicle route. In our experiments, two different PSO algorithms (global and local), and three construct algorithms are investigated for omparison. Computational results show that global PSO algorithm with Solomon insertion heuristics is more efficiency than the others.