Developed Particle Swarm Optimization Algorithm Research Articles

Nonlinear inversion of electrical resistivity sounding data for multi-layered 1-D earth model using global particle swarm optimization (GPSO)

Interpreting geophysical data requires solving nonlinear optimization problem(s) in inversion. Analytical methods such as least-square have some intrinsic limitations, which include slow convergence and dimensionality, making heuristic-based swarm intelligence a better alternative. Large-scale nonlinear optimization problems in inversion can be solved effectively using a technique within the swarm intelligence family called Particle Swarm Optimization (PSO). This study evaluates the inversion of geoelectrical resistivity data with global particle swarm optimization (GPSO). We attempted to invert field vertical electrical sounding data for a multi-layered 1-D earth model using the developed particle swarm optimization algorithm. The result of the PSO-interpreted VES data was compared with that of the least square inversion result from Winresist 1.0. According to the PSO-interpreted VES results, satisfactory solutions may be attained with a swarm of 200 or fewer particles, and convergence can be reached in fewer than 100 iterations. The GPSO inversion approach has a maximum capacity of 100 iterations, more than the least square inversion algorithm of the Winresist, which has a maximum capacity of 30 iterations. The misfit error of GPSO inversion is 6.14×10−7, much lower than that of the least square inversion of 4.0. The GPSO inversion model has lower and upper limit values of the geoelectric layer parameters model to fit the true model better. The limitations of the developed PSO inversion scheme include a slower execution time of the inversion procedures than the least-square inversion. There is a need for a priori knowledge of the number of layers from borehole reports in the study area. The PSO inversion scheme, however, estimates inverted models closer to the true solutions with greater accuracy than the least-square inversion scheme.

Sizing of a stand-alone PV–wind–battery–diesel hybrid energy system and optimal combination using a particle swarm optimization algorithm

In this paper, the main objective is the simulation of the electric supply for homes in remote areas located in Morocco (Oujda and Ouarzazate), Spain (Granada), and Algeria (Bechar). This simulation study is divided into two ideas, the first one is to optimize the hybrid system under a varied number of houses and the second part is to fix it in chosen values. This work relied on the particle swarm optimization (PSO) method to optimize and analyze the proposed stand-alone photovoltaic/wind/diesel/battery hybrid system. MATLAB software is used to run the simulations and to carry out the optimal solutions of the optimized system using a developed PSO algorithm. The PSO approach contains five principal operator axes, which are problem definition, PSO initial parameters, initialization, PSO main loop, and then run of the algorithm. Optimizing the system component sizes and reaching the minimum cost of energy (COE) were the function objectives of this optimization study. Moreover, calculation of the hydrogen production from the generated energy by PV and wind systems is performed.

A long-term intelligent operation and management model of cascade hydropower stations based on chance constrained programming under multi-market coupling

Under the medium- and long-term electric markets, cascaded hydropower stations face a series of practical challenges due to the uncertainty of inflow and market price. For long-term dispatch scheduling, the allocation of power generation in multimarkets is critical, including clean energy priority consumption market, inter provincial market and intra provincial market in order to maximize the operator’s expected revenue and reduce the market operation risks. Based on the hydro-dominant electricity market structure and settlement rules, we propose a long-term optimal operation method for cascade hydropower stations considering the uncertainty of multiple variables. First, a seasonal autoregressive integrated moving average model is used to handle the time-varying and seasonal characteristics of inflow series by using a copula connect function to fit the joint distribution of the monthly inflow, the clearing price of the intra provincial market and the delivery volume of the inter provincial market. Then, uncertain chance-constrained programming is established. Finally, a developed particle swarm optimization algorithm embedded in a Monte Carlo simulation is solved for hydropower operation policies, and the maximum revenue, resource allocation and scheduling strategy are obtained under the corresponding risk tolerance. Taking the actual data of cascaded hydropower stations in Yunnan Province, China, as an example, a simulation analysis is carried out. The results show that the proposed method can reasonably describe the uncertainty and correlation between the variables, realizing the optimal allocation of resources among multimarkets, and provide references for the long-term optimal operation of cascade hydropower stations in a multimarket environment. The results also show that the decision strategies should be determined considering the decision-maker’s risk preference.

Prediction of stable BC3N2 monolayer from first-principles calculations: Stoichiometry, crystal structure, electronic and adsorption properties

In this paper, a novel BC3N2 monolayer has been found with a graphene-like structure using the developed particle swarm optimization algorithm in combination with ab initio calculations. The predicted structure meets the thermodynamical, dynamical, and mechanical stability requirements. Interestingly, the BC3N2 plane shows a metallic character. Importantly, BC3N2 has an in-plane stiffness comparable to that of graphene. We have also investigated the adsorption characteristics of CO2 on pristine monolayer and Mo functionalized monolayer using density functional theory. Subsequently, electronic structures of the interacting systems (CO2 molecule and substrates) have been preliminarily explored. The results show that Mo/BC3N2 has a stronger adsorption capacity towards CO2 comparing with the pristine one, which can provide a reference for the further study of the CO2 reduction mechanism on the transition metal-functionalized surface as well as the new catalyst’s design.

Risk-averse supplier selection and order allocation in the centralized supply chains under disruption risks

This paper proposes a mixed-integer non-linear programming (MINLP) model for the integrated supplier selection and order allocation in a centralized supply chain considering the disruption risks and a risk-averse decision-maker. In order to capture a realistic scenario of considering the geographical characteristics of the suppliers, we assume that the suppliers belong to two regions: the buyer’s region (domestic suppliers) and outside of the buyer’s region (foreign suppliers). Considering this realistic feature, the supply chain might face two types of disruption risk: first, local disruption risks which might uniquely occur inside each supplier such as equipment breakdowns, and second, regional disruption risks that might occur in the region of the suppliers located in the same geographical region such as natural hazards. We formulate the problem considering a risk-neutral decision-maker as a benchmark, and then a risk-averse model is presented. In the latter case, we apply two types of risk assessment tools introduced in the finance literature to analyze the decision maker’s behavior: value-at-risk (VaR) and conditional value-at-risk (CVaR). We show that developed models are non-convex programming, and therefore, we apply the particle swarm optimization (PSO) algorithm as the solution approach. We also compare the developed PSO algorithm with the Genetic algorithm (GA) and the commercial GAMS solver to verify the efficiency of the solution method. The computational experiments indicate the impact of the decision maker’s attitude on the supplier selection and the order quantity.

Novel structural phase and superconductivity of W-Te compounds under high pressures

Novel structural phase and superconductivity of W–Te compounds have been investigated using the developed particle swarm optimization algorithm in combination with ab initio calculations. Under ambient conditions, WTe2 with Td phase is the most stable. with the pressure increasing, the 2H phase becomes stable until 60 GPa, and then WTe2 decompose into Te and WTe with the Pm3m phase. More interesting, the layered Cmcm phase of WTe3 is obtained at about 6 GPa, which remains stable up to 100 GPa. Moreover, electron–phonon calculations have been performed, which suggest that the Cmcm phase of WTe3 is superconducting and the critical superconducting temperature can reach about 3 K at 10 GPa.

Optimization of Bus Stops Locations Using GIS Techniques and Artificial Intelligence

Optimization of public transportation network in terms of reducing travel time and providing access to areas currently without sufficient access to the service facility would certainly motivate private car owners to use public transport. The reduction of vehicle numbers on the roads will undoubtedly lead to minimizing traffic congestion and reducing air pollution due to lesser exhaust emissions. This study used Geographic Information System (GIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to model the location of bus stops in Amman city in Jordan to find optimal travel time and serviceability of stops. The GIS modeling provided significant reduction of travel time for streets with redundant bus stops located at irregular distances, e,g. Zahran Street, where travel time was reduced by (23.25%) during off-peak hours and by 19.74% during peak hours, while PSO provided (28.95%) and 39% reductions, respectively. GA also provided significant reduction in travel time (47.96 %) for Zahran Street. For streets with insufficient number of bus stops, travel time went up due to increase in the number of stops, e.g. Al-Quds Street, where travel time increased from 12.25 min per route to 50.71 min due to increase in the number of stops from 6 to 25. In contrast, this increase in travel time significantly reduced the walking distance to the bus stop, from over 2000 m to approximately 400 m. Moreover, demand for bus stops serviceability significantly more than capacity. Average demand per bus stop on Al-Quds street, for example, was 10456 persons with a capacity of 1465.Comparison of the models by applying them to roads not included in the study showed consistent results that further confirmed the reliability of the models. The developed PSO algorithm and GA are easy to use for urban planning, where they can be applied to any existing network or planned development.

Optimal Battery Sizing of a Grid-Connected Residential Photovoltaic System for Cost Minimization using PSO Algorithm

This paper proposes a new optimization technique that uses Particle Swarm Optimization (PSO) in residential grid-connected photovoltaic systems. The optimization technique targets the sizing of the battery storage system. With the liberation of power systems, the residential grid-connected photovoltaic system can supply power to the grid during peak hours or charge the battery during non-peak hours for later domestic use or for selling back to the grid during peak hours. However, this can only be achieved when the battery energy system in the residential photovoltaic system is optimized. The developed PSO algorithm aims at optimizing the battery capacity that will lower the operation cost of the system. The computational efficiency of the developed algorithm is demonstrated using real PV data from Strathmore University. A comparative study of a PV system with and without battery energy storage is carried out and the simulation results demonstrate that PV system with battery is more efficient when optimized with PSO.

A new constrained PSO for fuzzy predictive control of Quadruple-Tank process

This paper presents a new constrained predictive controller based on TS modelling approach and Particle Swarm Optimization for optimal operation of nonlinear Multi Inputs Multi outputs system. The developed Particle Swarm Optimization algorithm is used to determine the predictive controls by minimizing a constrained multivariable criterion. The TS fuzzy modelling approach is applied to forecast the states of the process and to establish the conditions for the closed-loop stability. At the optimization stage, the proposed PSO provides the control action taking into account stability conditions and constraints. The performances of the developed method are tested during its application in the Quadruple-Tank process.Therefore, the experimental results demonstrate the strategy ability developed to control a nonlinear multivariable process. In addition, the closed-loop performance of the developed method is compared to that obtained with the nonlinear generalized predictive control method.

Analysis of complex gamma-ray spectra using particle swarm optimization

Analysis of gamma-ray spectra is an important step for identification and quantification of radionuclides in a sample. In this paper a new gamma-ray spectra analysis algorithm based on Particle Swarm Optimization (PSO) is developed to identify different isotopes of a mixed gamma-ray source and determine their fractional abundances. PSO is an iterative algorithm that imitates the social behaviors observed in nature to solve complex optimization problems. The PSO method is used for complex fitting to the response of a 3 × 3 inch NaI (Tl) scintillation detector and the fitting process is controlled by a test for significance of abundance and computation of Theil coefficient. To test the developed algorithm, a number of experimentally measured gamma-ray spectra related to a mixed gamma-ray source including different combinations of 60Co, 137Cs, 22Na, 152Eu and 241Am isotopes are analyzed using information of whole spectrum. The performance of the developed PSO algorithm is compared to the multiple linear regression (MLR) method as well. The results of the developed PSO algorithm show a better match with the real fractional abundances than that of MLR method.

On-Line Application of SHEM by Particle Swarm Optimization to Grid-Connected, Three-Phase, Two-Level VSCs with Variable DC Link Voltage

This paper is devoted to an otablen-line application of the selective harmonic elimination method (SHEM) to three-phase, two-level, grid-connected voltage source converters (VSCs) by particle swarm optimization (PSO). In such systems, active power can be controlled by the phase shift angle, and reactive power by the modulation index, against variations in the direct current (DC) link voltage. Some selected, low-odd-order harmonic components in the line-to-neutral output voltage waveforms are eliminated by calculating the SHEM angle set continuously through the developed PSO algorithm on field-programmable gate array (FPGA)-based computing hardware as the modulation index is varied. The use of powerful computing hardware permits the elimination of all harmonics up to 50th. The cost function of the developed PSO algorithm is formulated by using an optimum number of particles to obtain a global optimum solution with a small fitness value in each half-cycle of the grid voltage and then updating the SHEM angle set at the beginning of the next full-cycle. Since the convergence of the solution to a global minimum point depends upon the use of correct initial values especially for a large number of SHEM angles, a generalized initialization procedure is also described in the paper. Theoretical results are verified initially using hardware co-simulation. They are also tested using a small scale photovoltaic (PV) supply prototype developed specifically for this purpose. It is demonstrated that the 5th, 7th, 11th, 13th, 17th, and 19th sidekick harmonics are eliminated by on-line calculation of seven SHEM angles through the developed PSO algorithm on a moderately powerful XEM6010-LX150, USB-2.0-integrated FPGA module. All control and protection actions and the calculation of SHEM angles are achieved by a single FPGA chip and its peripherals within the FPGA board.

Mg3X2 (X = C, Si) monolayer in a honeycomb-kagome lattice: A global minimum structure

Two stable Two-dimensional (2D) material with a hexagonal honeycomb structure, i.e., the Mg3X2 (X = C, Si) monolayer have been predicted using a newly developed particle swarm optimization algorithm based on first-principles calculations. We found that the Mg3X2 exhibit a Dirac feature which arises from the pz orbitals of Mg and X atoms under the nonmagnetic (NM) state. The Dirac cone are not affected by stress strains along both the x and y directions. After considering the magnetic configuration, the ground state of Mg3X2 is antiferromagnetic (AFM) state. This magnetic configuration breaks the Dirac feature and causes a sizeable band gap in Mg3X2 monolayer. The newly predicted Mg3X2 is both mechanically and dynamically stable, implying its experimentally synthetic feasibility. The results are beneficial to this new 2D material in applications and providing a feasible strategy for 2D materials design.

Information Hiding using LSB Technique based on Developed PSO Algorithm

<p>Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most efficient and speed. An agents population is used in determining process of a required goals at search space for solving of problem. The (Dev.-PSO) algorithm is applied to different images; the number of an image which used in the experiments in this paper is three. For all used images, the Peak Signal to Noise Ratio (PSNR) value is computed. Finally, the PSNR value of the stego-A that obtained from blue sub-band colo is equal (44.87) dB, while the stego-B is equal (44.45) dB, and the PSNR value for the stego-C is (43.97)dB, while the vlue of MSE that obtained from the same color sub-bans is (0.00989), stego-B equal to (0.01869), and stego-C is (0.02041). Furthermore, our proposed method has ability to survive the quality for the stego image befor and after hiding stage or under intended attack that used in the existing paper such as Gaussian noise, and salt & pepper noise.</p>

A Modified PSO Algorithm for IIR Digital Filter Modeling

Modeling for an unknown plant is a very important and primary task in the control system engineering. By using this established model, an adequate controller can be further designed to meet certain control specifications. This paper will propose a new modeling method which is based on the infinite impulse response (IIR) digital filter structure. A modified particle swarm optimization (PSO) with multiple subpopulations is utilized to solve for the IIR filter coefficients to model an unknown digital system. The developed PSO algorithm is able to overcome the multimodal error surface problem that often occurs in the system modeling of IIR digital filter. Finally, three different kinds of digital systems are examined to demonstrate the feasibility of the proposed method, including the same system order and reduced order structures. It is concluded from several simulation results that the proposed method has an excellent performance on solving the IIR digital modeling problem.

Design and multi-physics optimization of rotary MRF brakes

Abstract Particle swarm optimization (PSO) is a popular method to solve the optimization problems. However, calculations for each particle will be excessive when the number of particles and complexity of the problem increases. As a result, the execution speed will be too slow to achieve the optimized solution. Thus, this paper proposes an automated design and optimization method for rotary MRF brakes and similar multi-physics problems. A modified PSO algorithm is developed for solving multi-physics engineering optimization problems. The difference between the proposed method and the conventional PSO is to split up the original single population into several subpopulations according to the division of labor. The distribution of tasks and the transfer of information to the next party have been inspired by behaviors of a hunting party. Simulation results show that the proposed modified PSO algorithm can overcome the problem of heavy computational burden of multi-physics problems while improving the accuracy. Wire type, MR fluid type, magnetic core material, and ideal current inputs have been determined by the optimization process. To the best of the authors’ knowledge, this multi-physics approach is novel for optimizing rotary MRF brakes and the developed PSO algorithm is capable of solving other multi-physics engineering optimization problems. The proposed method has showed both better performance compared to the conventional PSO and also has provided small, lightweight, high impedance rotary MRF brake designs.

Structural, mechanical and electronic properties of CaB2C2 at high pressure

Under high pressure, the graphite-like materials present complex structural behaviors, interesting mechanical properties, and fascinating electronic structures. We here report two novel high-pressure structures of oF40-CaB2C2 and HPtI20-CaB2C2 by using the newly developed particle swarm optimization algorithm based on first-principles calculations. The oF40-CaB2C2 and HPtI20-CaB2C2 exhibit a 3D sp3 B-C bonded network under compression, which is verified by the inspections of their crystal structures and partial densities of states. The study of mechanical anisotropy reveals that the degree of anisotropy for CaB2C2 is gradually enhanced with its structural transformations under pressure. Intriguingly, the originally indirect band-gap CaB2C2 at ambient pressure transform into a direct band-gap semiconductor under extreme pressure (beyond 214.4 GPa).

Correction of Terminal Velocity Prediction Model for CO<sub>2</sub>-Kerosene and Air-Kerosene Systems by Artificial Intelligence

In this study the essential factors of rising air and CO2 bubbles in distillated water and kerosene investigate with the experimental and theoretical attitude. Many formulas developed by pervious investigators for bubble terminal velocity prediction in air-water system. By using PSO (particle swarm optimization) algorithm and plotting experimental data of terminal velocity against the size of gas bubbles, suitable was chosen. Results showed that Jamialahmadi model is more practical for air-water and CO2-water system. The main aim of this paper is to validate and correct Jamialahmadi model for predicting of bubble’s terminal velocities in air-kerosene and CO2-kerosene systems. Jamialahmadi model requires a modification to be utilized for air-kerosene and CO2-kerosene system. The developed PSO algorithm model is accurate for prediction of experimental data with an average R2 value of 0.976.

Ground-State Structure and Physical Properties of NB2 Predicted from First Principles**Supported by the Natural Science Foundation of Henan Educational Committee under Grant No 2011A140006, and the Key Scientific and Technological Project of He'nan Province under Grant No 152102210307.

Using the newly developed particle swarm optimization algorithm on crystal structural prediction, we predict a new class of boron nitride with stoichiometry of NB2 at ambient pressure, which belongs to the tetragonal I4̄m2 space group. Then, its structure, elastic properties, electronic structure, and chemical bonding are investigated by first-principles calculations with the density functional theory. The phonon calculation and elastic constants confirm that the predicted NB2 is dynamically and mechanically stable, respectively. The large bulk modulus, large shear modulus, large Young's modulus, and small Poisson's ratio show that the I4̄m2 NB2 should be a new superhard material with a calculated theoretical Vickers hardness value of 66 GPa. Further analysis on density of states and electron localization function demonstrate that the strong B–B and B–N covalent bonds are the main reason for its high hardness in I4̄m2 NB2.

Parameter Identification of PMSMs Using Experimental Measurements and a PSO Algorithm

This paper introduces a particle swarm optimization (PSO) algorithm that uses experimental measurements for the identification of the direct ( $d$ ) and quadrature ( $q$ ) stator inductances and the stator resistance of permanent-magnet synchronous machines (PMSMs). Strategies for the identification of stator resistance and flux linkage of permanent magnets are also presented. The developed PSO algorithm has the advantages of fast and stable convergence characteristics, and it is relatively easy to implement; moreover, neither voltage source inverters nor closed-loop control strategies are needed. In this paper, the experimental results obtained by PSO are compared with other ones obtained through a current decay test to demonstrate the effectiveness of the presented method. A 32-bit C2000 real-time microcontroller and a commercial surface-mounted PMSM have been used to carry out the experimental measurements.

Novel Superconducting Phases of HCl and HBr under High Pressure: An Ab Initio Study

The novel superconducting C2/m phases of HCl and HBr were predicted using the developed particle swarm optimization algorithm for crystal structure prediction. The calculated results showed that the phase transition from P-1 to C2/m phases occurred at 250 and 120 GPa for HCl and HBr, respectively. Hydrogen bond symmetrization was observed in the newly predicted C2/m phase. No imaginary phonon frequencies in the whole Brillouin zone indicated that the C2/m phases of HCl and HBr were dynamically stable. In addition, the C2/m phase of HBr was the most stable structure, which rejected the earlier hypotheses of elemental decomposition into bromine and hydrogen. Perturbative linear response calculations predicted that the critical temperature Tc of the C2/m phase of HCl could reach 20 K at 250 GPa.