Swarm Optimization Method Research Articles

Optimization of the maximum power point of photovoltaic working under partial shading conditions

Photovoltaic is used to convert electricity from solar radiation. The working characteristics of photovoltaic depend on environmental conditions such as temperature, solar radiation intensity, and the surrounding environment. During operation, the photovoltaic generation system (PGS) can be partially or completely shaded due to natural phenomena such as clouds, buildings, dust, animals, electric pillars, trees ... these are changing the characteristics of the system’s power output of PV. This paper proposes a maximum power point tracking algorithm for PGS operating under partially shaded condition (PSC) based on Particle Swarm Optimization (PSO) method, and a configuration comprises of three PV modules type PHM60W36 is used to simulate using PSIM software. The study focused on changing the working characteristics of the photovoltaic system when changing factors such as level, location of the photovoltaic module are shaded. The effectiveness of the proposed method is not only compared with the traditional Perturb and Observe (P&O) method but also compared with those proposed previously under the same operating conditions. In addition, an experimental model was developed to investigate the response of the proposed solution in the real environment with the Chroma-62050H simulator. The results show the superiority of the proposed solution in improving the performance MPPT and convergence speed of the system under complex operating conditions.

Traffic Urban Control Using an Intelligent PSO Algorithm Based on Integrated Approach

In this paper, we intend to contribute to the improvement of urban traffic mobility using a learning method of traffic lights controllers. We proposed a Particle Swarm Optimization (PSO) method in which the intelligent swarm acts as the cycle time of the traffic signal. The best swarm (solution found) meets the evaluation criteria selected to describe desired objectives. The main measures of traffic lights efficiency are to maximize flow-rate at which vehicles can cross a road junction and minimize the additional travel time of the driver called vehicle delay. Particle Swarm Optimizer was coupled with the traffic flow model based on Continuous Petri nets (PN). One potential advantage of CPN model is to provide insights regarding a behavior of the platoon of vehicles on the target road network. The result obtained from this study has tested with various scenarios related to intersections in different situations. The developed self-scheduling of the optimal signal timing ensures safety and continuous traffic flow, thus increasing the mobility and reducing fuel consumption and pollutant emissions.

A chaotic optimization method based on logistic-sine map for numerical function optimization

Meta-heuristic optimization algorithms have been used to solve mathematically unidentifiable problems. The main purpose of the optimization methods on problem-solving is to choose the best solution in predefined conditions. To increase performance of the optimization methods, chaotic maps for instance Logistic, Singer, Sine, Tent, Chebyshev, Circle have been widely used in the literature. However, hybrid 1D chaotic maps have higher performance than the 1D chaotic maps. The hybrid chaotic maps have not been used in the optimization process. In this article, 1D hybrid chaotic map (logistic-sine map)-based novel swarm optimization method is proposed to achieve higher numerical results than other optimization methods. Logistic-sine map has good statistical result, and this advantage is used directly to calculate global optimum value in this study. The proposed algorithm is a swarm-based optimization algorithm, and the seed value of the logistic-sine map is generated from local best solutions to reach global optimum. In order to test the proposed hybrid chaotic map-based optimization method, widely used numerical benchmark functions are chosen. The proposed chaotic optimization method is also tested on compression spring design problem. Results and comparisons clearly show that the proposed chaotic optimization method is successful.

PSO to Solve the Problem of Integrated Economic & Emissions Dispatch

An important role in the electricity industry has alwa ys been efficient & optimum economical function of electricity p owersystems. This field of concern has taken a new turn in recent years, as environmental issues are being gradually discussed by t he public. Through optimizing distribution of load between power plant, the adverse ecological effects of outlet of gaseous contaminants will be reed. It contributes however to a significant increase in the functional cost of the system. Everything must therefore be equilibrium. There are three techniques investigated for solving the Combination of Economic and Emission Dispatch (CEED) solution that is one traditional method-Lambda Iteration System & Particle Swarm Optimization (PSO). The prize factor method which will change the many-objective CEED solution into one single minimal solution. The survey of total operating costs, total emissions, and system losses on 6-generation units test systems. This is carried out. Results show that Particle Swarm Optimization (PSO) method is superior to the other Lambda Iteration technique for reducing operating costs in solving the problem of Combined Current Economic &Emission Dispatch (CEED). Therefore, PSO technique is highly suited and recommended for use online.

A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection

Survey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.

Effects of Strontium incorporation to Mg-Zn-Ca biodegradable bulk metallic glass investigated by molecular dynamics simulation and density functional theory calculation

Molecular dynamics (MD) simulation and density functional theory (DFT) calculations were used to predict the material properties and explore the improvement on the surface corrosion resistance for the Mg66Zn30Ca3Sr1 bulk metallic glass (BMG). The Mg66Zn30Ca4 BMG was also investigated to realize the influence of the addition of Sr element on the material behaviors of Mg66Zn30Ca4. The Mg-Zn-Ca-Sr parameters of the next nearest-neighbor modified embedded atom method (2NN MEAM) potential were first determined by the guaranteed convergence particle swarm optimization (GCPSO) method based on the reference data from the density functional theory (DFT) calculation. Besides, using the 2NN MEAM parameters of the Mg-Zn-Ca-Sr system, the structures of Mg66Zn30Ca4 and Mg66Zn30Ca3Sr1 were predicted by the simulated-annealing basin-hopping (SABH) method. The local atomic arrangements of the predicted BMG structures are almost the same as those measured in some related experiments from a comparison with the calculated and experimental X-ray diffraction (XRD) profiles. Furthermore, the HA index analysis shows that the fractions of icosahedra-like local structures are about 72.20% and 72.73% for Mg66Zn30Ca4 and Mg66Zn30Ca3Sr1, respectively, indicating that these two BMG structures are entirely amorphous. The uniaxial tensile MD simulation was conducted to obtain the stress-strain relationship as well as the related mechanical properties of Mg66Zn30Ca4 and Mg66Zn30Ca3Sr1. Consequently, the predicted Young’s moduli of both BMGs are about 46.4 GPa, which are very close to the experimental values of 48.8 ± 0.2 and 49.1 ± 0.1 GPa for Mg66Zn30Ca4 and Mg66Zn30Ca3Sr1, respectively. However, the predicted strengths of Mg66Zn30Ca4 and Mg66Zn30Ca3Sr1 are about 850 and 900 MPa, both are slightly higher than the measured experimental values about 747 ± 22 and 848 ± 21 MPa for Mg66Zn30Ca4 and Mg66Zn30Ca3Sr1. Regarding the thermal properties, the predicted melting temperature of Mg66Zn30Ca3Sr1 by the square displacement (SD) profile is about 620 K, which is very close to the experimental melting temperature of about 613 K. The self-diffusion coefficients of Mg, Zn, Ca, and Sr elements were also calculated for temperatures near their melting points by means of the Einstein equation. The methodology can determine the diffusion barriers for different elements by utilizing these diffusion coefficients resulting in a fact that the diffusion barriers of Ca and Sr elements of Mg66Zn30Ca3Sr1 are relatively high. For the electronic properties predicted by the DFT calculation, the projected density of states (PDOS) profiles of surface Mg, Zn, Ca, and Sr elements clearly show that the addition of Sr into Mg66Zn30Ca4 effectively reduces the s and p orbital states of surface Mg and Zn elements near the Fermi level, particularly the p orbits, which suppresses the electron transfer as well as increases the surface corrosion resistance of Mg66Zn30Ca4. Consequently, this study has provided excellent 2NN MEAM parameters for the Mg, Zn, Ca, and Sr system by the GCPSO method to predict real BMG structures as well as by means of the DFT calculation to explore the electronic properties. Eventually, through our developed numerical processes the material properties of BMGs with different compositions can be predicted accurately for the new BMG design.

Dynamics of a True Moving Bed Reactor: Synthesis of n-Propyl Propionate and an alternative optimization method

This work investigates a novel synthesis route of n-Propyl Propionate using a Simulated Moving Bed Reactor (SMBR) packed with Amberlyst 46 resin. Simultaneously, it is presented a novel method for SMBR units design and optimization. The results demonstrate that the unit is capable of producing the n-Propyl Propionate with high purity and conversion, over 99 %. Furthermore, the productivity associated with its low consumption of eluent and its operation at significantly lower temperature provides evidence that the SMBR may be an efficient and competitive route to produce n-Propyl Propionate. Moreover, the swarm optimization method here used showed to be a powerful optimization tool while simultaneously providing deep information about the SMBR process feasible operating region.

Selection of the Capacitance of an Active Filter Capacitor and the Control Method for Its Controller

The method of choosing the optimal values of the capacitance of a capacitor of an active filter (AF) and the use of two different control methods of an AF to compensate for harmonics and reactive power in conditions that are abruptly variable, distorting the shape of the curve of the load current. The method of particle swarm optimization (PSO) and fuzzy logic are considered as methods of AF control. Both methods are built into the adaptive proportional–integral (PI) controller. PSO optimization is also used to select the optimal capacitance value of the AF capacitor. The operation of the PI controller is determined by the gain of the proportional and integrating component. The results of application of the PSO method and the fuzzy logic method for determining these coefficients in the PI controller are presented. The methods proposed for the management of an AF tried to compensate for currents of higher harmonics in conditions of abruptly variable, distorting the shape of the curve of the load current in the distribution network 66/20/0.4 kV industrial load. The advantages and disadvantages of each control method are shown, and, based on these results, a hybrid parallel controller (PSO–PI and PI fuzzy logic) is proposed that combines the advantages of both methods.

Meta-heuristic procedure for enhanced spectrum fragmentation assessment in elastic optical networks

This work proposes the use of a procedure for improving the loss of capacity assessment of heterogeneous bandwidth requests in the min slot-continuity capacity loss (MSCL) spectrum allocation algorithm. The particle swarm optimization (PSO) method is used to determine the best relationship between the number of requested slots and the amount of contiguous free slots in the requested and interfering paths. We also incorporate in the MSCL the influence of the number of hops of a route in its capacity evaluation. Considerable reductions in the path request blocking probability are achieved when the technique is applied, particularly when the number of hops is included to the evaluation.

A meta-heuristic for a bi-objective multi-commodity green distribution network

This paper presents a bi-objective optimisation model for solving the location-allocation problem (LAP) of a distribution network that consists of four echelons including suppliers, manufacturing plants, distribution centres and customers. Multiple product types and raw materials are considered. A bi-objective mathematical model is formulated to minimise the total costs and the greenhouse gas emissions arising from the location-allocation decisions. A meta-heuristic algorithm is developed to solve the proposed mathematical model. The proposed algorithm was implemented using multi-objective particle swarm optimisation (MOPSO) and multi-objective differential evolution (MODE) solution methods and results were analysed.

Inverse Modeling of Nonlinear Artificial Muscle Using Polynomial Parameterization and Particle Swarm Optimization

The properties of pneumatic artificial muscle (PAM) with excellent power‐to‐weight ratio and natural compliance made it useful for healthcare engineering applications. However, it has undesirable hysteresis effect in controlling a robotic manipulator. This behavior is quasistatic and quasirate dependent which changed with excitation frequency and external force. Apart from this, it also inherits frictional presliding behavior with nonlocal memory effect. These nonlinearities need to be compensated to achieve optimal performance of the control system. Even though an inverse modeling of PAM has limited application, it is important on certain control system implementation that requires the solution to the inverse problem. In this paper, the inverse modeling of PAM in the form of activation pressure was proposed. This activation pressure model was derived according to static pressure and extracted hysteresis components from pressure/length hysteresis. The derivation of the static pressure model follows the phenomenological‐based model of third‐order polynomial. It is capable of characterizing the nonlinear region of PAM at low and high pressure. The derivation of extracted hysteresis model follows the mechanism of dynamic friction. In this principle, the activation pressure model was dependent on regression coefficient of the static pressure model and dynamic friction coefficients of the extracted hysteresis model. The regression constants of these coefficients were characterized from the hysteresis dataset by using model parameter identification and the particle swarm optimization (PSO) method. The result from model simulation shows the root mean square error (RMSE) value of less than 10% error was evaluated at various excitation frequencies and external forces. This inverse modeling of PAM implemented a simple approach, but it should be useful in control design applications such as rehabilitation robotics, biomedical system, and humanoid robots.

Generalized Optimal and Explicit PI/PID Tuning Formulas for Underdamped Second-order Systems

This study aims to propose generalized formulas in an explicit form to optimally tune the gains of the proportional-integral (PI) and proportional-integral-derivative (PID) controllers of an underdamped second-order system. Unlike other methods used in literature, the controller gains are tuned explicitly based on the known system parameters of the second-order system, namely: the damping ratio and the natural frequency. The proposed accurate and one-step formulas do not need any further effort to effectively control any given second-order system while keeping the desired performance indices within near-optimal specifications. The proposed work relies on a restricted deadbeat response criterion and cascaded surface curve fitting procedure to end by near-optimal controller gains tuning formulas. A particle swarm optimization (PSO) method is employed to optimize the primary tuned gains for wide-range of system frequency. The proposed formulas are tested and simulated to demonstrate their effectiveness with comparisons with the closest common related gains tuning methods. Based on the comprehensive analysis, the proposed work ensures 2% maximum percentage overshoot when the proposed formulas are adopted to tune the gains of the PID controllers. Different comparisons are carried out to show the effectiveness of the proposed work.

Thermo-hydrodynamic analysis and optimal design of a GM cycle cryorefrigerator using response surface methodology and particle swarm optimization

In this investigation, the response surface methodology is implemented to analyze the influences of high and low pressure of the compressor, frequency and waiting time of the rotary valve on the cooling capacity and COP of Gifford-McMahon (GM) cryorefrigerator. A one-dimensional numerical model based on finite volume discretization method (FVM) of the governing equations is employed to calculate the cooling capacity and COP of the Gifford-McMahon cryorefrigerator. Analysis of variance (ANOVA) is performed to visualize the impact of the aforementioned input parameters on both cooling capacity and COP. Finally, the particle swarm optimization (PSO) method is opted to optimize the regression equations so obtained from the response surface analysis, to maximize the cooling capacity and COP of the Gifford-McMahon cryorefrigerator. Results reveal that both COP and cooling capacity are not able to produce maximum performance at a particular combination of those input parameters. At a lower pressure ratio, the COP is maximum, while at a higher pressure ratio the cooling capacity is maximum. Furthermore, at a nominal value of waiting time, the cooling capacity is maximum and a higher value of waiting time the COP is maximum. It is observed that, after optimizing the parameters by PSO, the cooling power increases by 11.3% and COP increases by 6.43%.

Situation prediction of large-scale Internet of Things network security

The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.

Optimization-based synthesis of a metamaterial electric cloak using nonhomogeneous composite materials

ABSTRACTIn the paper the synthesis of an electric cloak is discussed. The nonhomogeneous composite material was proposed as a realization of a stationary electric field metamaterial with anisotropic properties necessary for cloaking objects. The particle swarm optimization (PSO) method was applied for the synthesis of nonhomogeneous material composed of periodically arranged unit cells. Anisotropic properties of metamaterial were achieved by adjusting the internal geometry of a porous unit cell structure. Proposed elements have a wide range of effective electrical conductivities in orthogonal directions of an external static electric field. Optimization scheme was applied to design the required unit cell structure, defined by four geometrical parameters modified and adjusted by the PSO. The numerical models of the conductive background, non-conductive object and several types of cloaks were investigated. The results obtained by The Finite element method (FEM) were characterized with respect to theoretical values of the required conductivities and cloaking effectiveness.

Evolution algorithms applied for 3D beamforming UWB channels

In this paper, the main research object is evolution algorithm applied for an ultra-wideband three-dimensional (3D) circular antenna array and the significance of elevation angles and their influence on the system performance are highlighted. The 3D channel is calculated by means of 3D shooting and bouncing ray/image techniques which specify multipath elevation angles and azimuth for horizontal plane angles. The capacities of the two-dimensional (2D) and three-dimensional transmitting antenna arrays have been presented. Beam-synthesizing techniques are applied at the transmitter to focus the transmitter energy for the sake of mitigating the multi-path effect and improving channel capacity. A combination of asynchronous particle swarm optimization (APSO) and dynamic differential evolution (DDE) methods are introduced to make adjustment to the length of the feed line on each array element for the maximum capacity. In comparison with 2D azimuth-only antenna arrays, a higher directive gain can be achieved from the 3D antenna arrays results in high channel capacity. Besides, it is discovered that the capacity for the 3D array improves by as much as over 10% when compared to that for the 2D array. As revealed by numerical results, APSO is capable of obtaining better beam-forming pattern to improve the channel capacity than DDE by about 9%. Not only does our research provide qualitative outcomes, it also presents quantitative results.

Parameter estimation based on factor graph in wide‐band OFDM systems

The channel estimation in a wide-band orthogonal frequency division multiplexing (OFDM) system is different from the narrow-band system, for the estimation parameters should include the Doppler scale factor, which is a particular parameter of the wide-band system. The underwater acoustic OFDM system is a typical wide-band system, and its channel has more than one scale factor usually. For this multi-scale case, the common estimation method is a factor-by-factor process with repeated calculation of the reproduced waveform. This article proposes a factor-graph-based estimation method, which fully uses the restriction relationship among the parameters, and estimates the parameters simultaneously. To reduce the computation load, the modified Bayesian propagation (BP) algorithm is proposed. Comparing to the common BP algorithm, the modified BP algorithm simplifies the calculation of the condition probability in the summation. The simulation results of the mean squared error show that the proposed method is an effective estimation method for the multi-scale wide-band system. The bit error rate performance of the proposed method is very close to the result of the known channel-state-information, and outperforms the fractional Fourier transform (FrFT) and the modified particle swarm optimisation (MPSO) methods for well channel condition.

QSPR based ANFIS model for predicting standard molar chemical exergy of organic materials

Prediction of standard molar chemical exergy value for organic compounds is investigated using a quantitative structure-property relationship (QSPR) model combined with adaptive neuro-fuzzy inference system (ANFIS) strategy. Particle swarm optimization (PSO) method is also implemented to determine the optimal ANFIS structure. The proposed model uses three constitutional descriptors in model development’s procedure. The QSPR-ANFIS model represents a great performance in prediction of the standard molar chemical exergy values with 0.9999, 44548, and 1.49 values for R2, RMSE, and %AARD, respectively.

Design chip position of printed circuit board based on particle swarm optimization

Printed Circuit Board (PCB) is a critical component in the IC packaging process. Manufactured PCBs are typically designed for use by a specific kind of chip and cannot be used for multiple chip types. However, allowing different kinds of chips to be served by a single PCB would greatly improve material preparation and production processes. However, the design and manufacturing of PCB for use in multiple chip types raises issues related to minimizing the use of gold wire. Traditional manual methods rely extensively on experience. This paper applies Particle Swarm Optimization (PSO) methods to identify optimal positioning with experimental results showing a 20% reduction of gold wire in multi-use PCB.

Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods

High dimensional problems are often encountered in studies related to hyperspectral data. One of the challenges that arise is how to find representations that are accurate so that important structures can be clearly easily. This study aims to process segmentation of hyperspectral image by using swarm optimization techniques. This experiments use Aviris Indian Pines hyperspectral image dataset that consist of 103 bands. The method used for segmentation image is particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO) and fractional order Darwinian particle swarm optimization (FODPSO). Before process segmentation image, the dimension of the hyperspectral image data set are first reduced by using independent component analysis (ICA) technique to get first independent component. The experimental show that FODPSO method is better than PSO and DPSO, in terms of the average CPU processing time and best fitness value. The PSNR and SSIM values when using FODPSO are better than the other two swarm optimization method. It can be concluded that FODPSO method is better in order to obtain better segmentation results compared to the previous method.