PSO Method Research Articles

Event-Based Rainfall Interception Modeling in a Cerrado Riparian Forest—Central Brazil: An Alternative Approach to the IS Method for Parameterization of the Gash Model

Although being the second largest biome in South America, the Cerrado biome is understudied in terms of hydrological modeling of the interception processes of its different vegetation types. To better understand its net precipitation components, high-temporal-resolution throughfall data were used to parameterize the Gash Analytical Model on a per storm basis. Two different methods (Single Event Regression—SI, and Particle Swarm Optimization—PSO) for parametrizing the Gash model were tested. Simulated throughfall from 35 events showed the Nash coefficient ranging from 0.779 to 0.989, confirming the good phenomenological approach of the Gash model. One of the most important results presented in this work was the analysis of the variability of the parameters. Considering the PSO method, we observed a mean free throughfall (ρ) of 0.17, with a confidence interval ranging from 0.16 to 0.18. The storage (S) confidence intervals ranged from 1.11 mm to 1.62 mm, with a mean value of 1.37 mm. The observed saturation depth (P′G) mean value was 1.66 mm, with a confidence interval from 1.35 mm to 1.97 mm. Finally, the mean evaporation precipitation rate (E/R) was of 0.20, with confidence bands from 0.15 to 0.24. Overall, there were observed higher losses from the Cerrado compared to other Brazilian biomes such as the Amazon and Atlantic Forest. These findings can contribute to the parameterization of hydrological models applied to the Brazilian Cerrado.

The geometry, electronic and magnetic properties of VLi n (n = 2–13) clusters using the first-principles and PSO method

ABSTRACT In this paper, the growth pattern, magnetic and electronic structure of VLi n (n = 2–13) clusters are studied using density functional theory in generalised gradient approximation. The growth pattern showed that the VLi n (n = 2–13) clusters favoured the three-dimensional configuration and appeared cage-like structures with increasing size. The average binding energy, second-order energy difference and dissociation energy are discussed, and the VLi12 cluster is magnetic superatom. The spin magnetic moment of VLi12 cluster is 1 μ B and the molecular orbital arrangement is 1S21P61Dα 51Dβ 4. The chemical bonding patterns of the VLi12 cluster was obtained by using molecular orbital analysis and the adaptive natural density partitioning method.

A comparative study of the energy, exergetic and thermo-economic performance of a novelty combined Brayton S-CO2-ORC configurations as bottoming cycles

This paper presents a comparative study on the energy, exergetic and thermo-economic performance of a novelty thermal power system integrated by a supercritical CO2 Brayton cycle, and a recuperative organic Rankine cycle (RORC) or a simple organic Rankine cycle (SORC). A thermodynamic model was developed applying the mass, energy and exergy balances to all the equipment, allowing to calculate the exergy destruction in the components. In addition, a sensitivity analysis allowed studying the effect of the primary turbine inlet temperature (TIT, PHIGH, rP and TC) on the net power generated, the thermal and exergy efficiency, and some thermo-economic indicators such as the payback period (PBP), the specific investment cost (SIC), and the levelized cost of energy (LCOE), when cyclohexane, acetone and toluene are used as working fluids in the bottoming organic Rankine cycle. The parametric study results show that cyclohexane is the organic fluid that presents the best thermo-economic performance, and the optimization with the PSO method conclude a 2308.91 USD/kWh in the SIC, 0.22 USD/kWh in the LCOE, and 9.89 year in the PBP for the RORC system. Therefore, to obtain technical and economic viability, and increase the industrial applications of these thermal systems, thermo-economic optimizations must be proposed to obtain lower values of the evaluated performance indicators.

Optimization procedure for parameter determination of caboche kinematic hardening model

The ratcheting behavior and prediction of 316L stainless steel pipe was developed using numerical finite elementapproach. The Chaboche model parameters to be used in the numerical method were adopted from symmetric post monotonic teston the pipe. The significant property, the elastic limitof the material,was extracted using incremental cyclic uniaxial test. Comparison between two well-known optimization methods, Particle Swarm Optimization and Genetic Algorithm gave the former a slightly better correlation with experimental tests.The Chaboche model in ANSYS was utilized to predict the uniaxial ratcheting behavior of the pipe specimen. Theclose comparison of rate of ratcheting between finite element simulation and experimental test indicates that accurate elastic limit extraction from post monotonic test alone results in improved ratcheting prediction, with PSO method adopted to optimize ratcheting parameters.

RETRACTED ARTICLE: A discrete wavelet transform and recurrent neural network based medical image compression for MRI and CT images

Medical imaging is an active and developing field that has an impact on recognition, diagnosis and surgical planning of the disease. The image compression is introduced in the medical imaging for decreasing the redundancies to avoid storage and bandwidth related problems. In this research, Discrete Wavelet Transform and Recurrent Neural Network based compression techniques are used through brain images for achieving better compression rate with less loss. The region growing and otsu thresholding are used to separate the ROI image and Non ROI image. The DWT and RNN are used for the ROI and non ROI portions of the medical image. Additionally, the local attributes of RNN is enhanced by Gravitational Search Algorithm and Particle Swarm Optimization. The performance of this proposed method is calculated as peak signal to noise ratio, mean square error, compression ratio and space saving percentage. The existing techniques used to compare the proposed method are Fractional Order Darwinian PSO, Quasi Fractal and Oscillation Method and Burrows Wheeler Transform—move to front transform. The PSNR of the proposed method is 35.2362 dB which is high when compared to the existing methods.

An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system

Photovoltaic (PV) is a source of electrical energy derived from solar energy and has a poor level of efficiency. This efficiency is influenced by PV condition, weather, and equipments like Maximum Power Point Tracking (MPPT). MPPT control is widely used to improve PV efficiency because MPPT can produce optimal power in various weather conditions. In this paper, MPPT control is performed using the Fuzzy Logic-Particle Swarm Optimization (FL-PSO) method. This FL-PSO is used to get the Maximum Power Point (MPP) and minimize the output power oscillation from PV. From the simulation results using FL-PSO, the values of voltage, and output power from the boost converter are 183.6 V, and 637.7 W, respectively. The ripple of output power from PV with FL-PSO is 69.5 W. Then, the time required by FL-PSO reaches MPP is 0.354 s. Compared with MPPT control based on the PSO method, the MPPT technique using FL-PSO indicates better performance and faster than the PSO.

Improved SVM algorithm for upper limb rehabilitation mechanical arm Study on the Prediction of Track Tracking Control

Aiming at the strong coupling, nonlinear and time-varying characteristics of upper limb rehabilitation training manipulator, a trajectory tracking predictive controller based on SVM (support Vector Machine) is designed. The input and output data of the manipulator system are collected, and the generalized inverse system is obtained by SVM identification, which is decoupling from the original system in series. For the decoupling system, the improved SVM algorithm is used to predict the trajectory tracking control, and the SVM algorithm is improved by combining the predictive function control method of PSO optimized rolling control sequence. The improved SVM algorithm can predict the trajectory of upper limb rehabilitation manipulator with high precision, and the experimental results show that the improved algorithm has good adaptability to the stability and robustness of the system.

Novel control topology with obstacle detection using RDPSO–GBA in mobile AD-HOC network

MANET plays an irreplaceable task by providing quality aware broadcast routing technique for small infrastructure-less field of application. Depending upon arising interference, transmission power of individual node can be done by Ant Colony Optimization (ACO) algorithm. In addition, nodes probability link connectivity is estimated by using Bayesian pursuit (BPST) algorithm that determines probability to control adjustment in nodes power. In this paper, proposed to control topology with obstacle detection using RDPSO–GBA (Robotic Darwin Particle Swarm Optimization–Graph Based Algorithm) in mobile ad hoc network. The robotics DPSO is to be represented with the help of the multiple swarms of various testing results when each swarm particularly operates an ordinary PSO method with various set of rules which are governing the gather of swarms. In the graph based algorithm, the nodes are to be presented in the MANET which has been separated into the two various classes that is leaders and the followers. The leader has the base node and the explorer node and the follower has the autonomous robots which provide the communication broadcasting. The simulation and result shows that there is the analysis of the various performances which is compared with conventional methods.

Cold-Chain Logistics Distribution Routing Optimization Based on PSO

Recently, cold chain which aims to keep agricultural product fresh before arrive to the designated customers has been greatly developed. The distribution routing optimization problem of cold chain has been an attractive research hotspot. In this paper, we introduce the basic PSO method and it’s advantaged about good robustness, global convergence, and intelligent search. Then, we put forward a method for distribution path optimization based on PSO. Taking an example, the evaluation results show that our proposed method can achieve the optimal solution effectively.

Improving the PSO method for global optimization problems

The paper introduces two modifications for the well-known PSO method to solve global optimization problems. The first modification deals with the termination of the method and the second with the bounding of the so-called velocity in order to prevent the method from creating particles outside the domain range of the objective function. The modified method was tested on a series of global optimization problems from the relevant literature and the results are reported.

Performance enhancement of solar PV systems applying P&O assisted Flower Pollination Algorithm (FPA)

In recent years, designing reliable and practically feasible Maximum Power Point Tracking (MPPT) techniques to maximize the power output of PV power plants has become a crucial research objective. Further, to counteract the inimitable PV operating conditions, numerous bio-inspired metaheuristic algorithms have been proposed in literature; that are predominantly complex and difficult to implement. On the other hand, the age old Perturb and Observe (P&O) technique is way superior owing to its simplicity, robustness and reduced switching stress. In this context, bio inspired methods assisted by P&O can be a viable solution to enhance the efficiency and reliability of MPPT algorithms. Unfortunately, the potential of hybrid techniques is less explored in literature and moreover, the strategy utilized to switch between bio-inspired and P&O method is rather superficial; that has not been proven judicially. Therefore, in this article, a new FPA method assisted by P&O is proposed. A new switching strategy is incorporated and validated in this work to achieve effective utilization of both FPA and P&O algorithms. More importantly, the transition is only initiated when global power regions are initially explored with FPA. Further, for truthful comparison, the results of FPA-P&O are compared with recently proven Enhanced Leader Particle Swarm Optimization (ELPSO) and conventional PSO methods.

Global maximum power point tracking of solar PV strings using the teaching learning based optimisation technique

ABSTRACT The rapid developing smart cities in India increase demand for electric power generation using renewable energy sources. Sun irradiation that falls on the solar photovoltaic (PV) panel has different P-V and I-V characteristics. This work presents the extraction of global maximum power point tracking of a PV system using the novel population-based optimisation method called the teaching learning based optimisation (TLBO) technique inspired by the class room teaching environment. The proposed method overcomes the shortfalls of other conventional MPPT tracking methods in view of their tracking efficiency and steady-state oscillations. TLBO and PSO algorithms were compared based on the number of fitness evaluations rather than that of the number of population under different environmental conditions. Also, the proposed research work was analysed in a qualitative and quantitative approach through matlab/simulink, respectively. It is found that TLBO met the heuristic method requirements, showed better performance compared with the PSO method with less computational effort and good consistency.

COMPARISON OF MPPT ALGORITHMS FOR PHOTOVOLTAIC SYSTEMS UNDER UNIFORM IRRIADIANCE BETWEEN PSO AND P&O

This paper shows a Comparison between Conventional Method [P&O] and particle swarm optimization [PSO] Based on MPPT Algorithms for Photovoltaic Systems under uniform irradiance and temperature. The main idea is to show that PSO method has a very high tracking speed and has the ability to track MPP under different environmental conditions in addition to an easy hardware implementation using a low-cost microcontroller. MATLAB simulations are carried out under very challenging conditions, namely irradiance and temperature, which reflect a change in the load [KW]. The proposed PSO tracking method Results will be compared with conventional method called [P&O] through MATLAB/SIMULINK.

Minimization of Power Losses with Different Types of DGs Using CSA

Proper allocation of DGs in distribution network is important from all the aspects like technical, economical and environmental. In this paper, Crow search algorithm (CSA) technique has been proposed to find the optimal size and location of multiple DGs of different types to reduce the active power loss of the distribution network (DN). The proposed method has only two parameters to be tuned. The proposed method is tested on 33 bus and 69 bus test system and the obtained result is compared with IA and PSO method. The proposed method gives better results compared to existing PSO and IA methods.

Simultaneous Allocation of Multiple Distributed Generators and Shunt Capacitor Banks in Radial Distribution Systems using Grasshopper Optimization Algorithm

Electricity has become a part of day to day life of the people. With the increase in population, electricity demand is also raised. The development of distribution systems (DSs), the optimal utilisation of shunt capacitor banks (SCBs) and distributed generators (DGs) in the DSs are heightened. In this paper, a methodology is proposed to allocate both DGs and SCBs simultaneously on the DS. The objectives are to minimise power loss, improving the voltage profile and voltage stability index (VSI) of the DS. First locations of DGs and SCBs are found out by loss sensitivity factor (LSF) technique. Next grasshopper optimisation algorithm (GOA) is used to determine the rating of these sources. The methodology is implemented on IEEE 33 and 69 bus test systems considering optimal allocation of multiple DGs without and with placement of capacitors. Finally, the results obtained by the proposed method are compared with GA/PSO and PSO methods.

Optimized path planning of an unmanned vehicle in an unknown environment using the PSO algorithm

Today, the use of drones has expanded, particularly in high-risk and/or inaccessible environments, or situations where the cost of human resource is high. One of the most important uses of these unmanned vehicles is in rescue fields where they carry instruments and resources, or transfer wounded people. One of the most important discussions in this regard is the issue of routing these cars in an unknown environment. The first step for the vehicle to start its mission is to drive around environmental barriers. Environmental barriers can be divided into two categories. The first category comprises barriers that can be located on a map using satellite imagery and known maps. The second category contains obstacles that the car may encounter while navigating the path and but which have not been anticipated. To solve this problem, this research uses the PSO method to optimize offline routing in an environment with a specified map. The vehicle then may encounter new obstacles when moving on the planned path and identify those obstacles using sensors. In this case, using the neural network algorithm, it can obtain an optimal pseudo-path to circumvent the obstacle. In fact, the issue is divided into two sections. The first issue is the optimal routing with the PSO method, and the second section tackles the problem of dealing with unplanned obstacles using the neural network algorithm.

Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia

This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO ( A W P S O c f ) and sigmoid function PSO ( S F P S O c f ) , are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed A W / S F P S O c f methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The performance results showed the superiority of S F P S O c f in terms of accuracy is selecting an optimal configuration at fitness function value 0.031268, LLP value 0.002431, LCC 53167 USD, and LCE 1.6413 USD. The accuracy of A W / S F P S O c f methods is verified by using the iterative method.

Airfoil Optimization of Land-Yacht Robot Based on Hybrid PSO and GA

Airfoil optimization algorithm is studied and a hybrid PSO and GA method is proposed in this paper. After function test, it shows that algorithm is well in convergence performance, fast speed, and optimization capability. Then, the airfoil parametric expression theory is analyzed. A new airfoil is obtained after combining CFD and PSO-GA optimization. The aerodynamic of new airfoil is compared with the airfoil optimized by GA-PSO and basic airfoil NACA0018. The results indicate that new airfoil is better than the other two airfoils in lift coefficient, lift-drag ratio, and surface pressure. At last, wing-sail of new airfoil and NACA0018 wing-sail are designed and manufactured. Both of them are applied in land-yacht robot linear motion and steering motion experiment. For the linear motion, in the situation of wind speed being 15[Formula: see text]m/s and angle of attack being 5, running speed of robot with optimized new wing-sail is 1.853[Formula: see text]m/s. In steering motion, trajectory with new wing-sail is closer to the real situation and it gets more thrust. The experiments data verify that the simulation results are correct and PSO-GA algorithm is effective.

Introducing languid particle dynamics to a selection of PSO variants

Previous research showed that conditioning a PSO agent’s movement based on its personal fitness improvement enhances the standard PSO method. In this article, Languid Particle Dynamics (LPD) technique is used on five adequate and widely used PSO variants. Five unmodified PSO variants were tested against their LPD-enabled counterparts on three search space dimensionalities (10, 20, and 50 dimensions) and 30 test functions of the CEC 2014 benchmark test. In the preliminary phase of the testing four of the five tested PSO variants showed improvement in accuracy. The worst and best-achieving variants from preliminary test went through detailed investigation on 220 and 770 combinations of method parameters, where both variants showed overall gains in accuracy when enhanced with LPD. The results obtained with best achieving PSO parameters were subject to statistical analysis which showed that the two variants give statistically significant improvements in accuracy for 13–50% of the test functions. Lastly, an engineering application test-case of water distribution system optimization was used, in which 151 pipe diameters were optimized with the tested PSO variants. Two of the five PSO variants manifested significantly improved accuracy when enhanced with LPD, with a LPD-enabled variant producing the overall best results, as being the only variant capable of finding the optimal pipe network configuration.

Multi-model based PSO method for burden distribution matrix optimization with expected burden distribution output behaviors

Burden distribution is one of the most important operations, and also an important upper regulation in blast furnace &#x0028 BF &#x0029 iron-making process. Burden distribution output behaviors &#x0028 BDOB &#x0029 at the throat of BF is a 3-dimensional spatial distribution produced by burden distribution matrix &#x0028 BDM &#x0029 , including burden surface output shape &#x0028 BSOS &#x0029 and material layer initial thickness distribution &#x0028 MLITD &#x0029. Due to the lack of effective model to describe the complex input-output relations, BDM optimization and adjustment is carried out by experienced foremen. Focusing on this practical challenge, this work studies complex burden distribution input-output relations, and gives a description of expected MLITD under specific integral constraint on the basis of engineering practice. Furthermore, according to the decision variables in different number fields, this work studies optimization of BDM with expected MLITD, and proposes a multi-mode based particle swarm optimization &#x0028 PSO &#x0029 procedure for optimization of decision variables. Finally, experiments using industrial data show that the proposed model is effective, and optimized BDM calculated by this multi-model based PSO method can be used for expected distribution tracking.