Particle Swarm Optimization Approach Research Articles

An improved equivalent beam model of large periodic beam-like space truss structures

Space truss structures are essential components for space-based remote sensing loads with high spatial and temporal resolutions. To achieve high-precision vibration control, an accurate and efficient dynamics model is essential. In addition to the current equivalent beam model (EBM) based on the classical continuum theory, an improved equivalent beam model (IEBM) is proposed that considers the impact of the distinction between trusses and beams on torsional and shear deformations, as well as the impact of shear deformation on flexural rigidity. According to the displacement expressions of spatial beams, torsional, shear, and bending correction coefficients are introduced to derive expressions of strain energy and kinetic energy. The energy equivalence principle is then utilized to calculate the elasticity and inertia matrices, and dynamics equations are established using the finite element method. Subsequently, an IEBM is constructed by employing the particle swarm optimization approach to determine the correction coefficients with the truss natural frequency as the optimization target. The natural vibration characteristics of the structure are estimated for various material properties. Compared with the full-scale finite element model, the EBM reaches a maximum error of 80% for a low modulus of elasticity, while the maximum error of the IEBM is less than 2% for any given parameters, indicating its superior accuracy to the EBM.

Nonlinear Adaptive Back-Stepping Optimization Control of the Hydraulic Active Suspension Actuator

The displacement tracking performance of the electro-hydraulic servo actuator is critical for hydraulic active suspension control. To tackle the problem of slow time-varying parameters in the existing actuator dynamics model, a nonlinear adaptive back-stepping control (ABC) approach is adopted. Simultaneously, the parameters of the nonlinear ABC are difficult to configure, resulting in a poor control effect. An enhanced particle swarm optimization (PSO) approach integrating crazy particles (CP) and time-varying acceleration coefficients (TVAC) is suggested to optimize the controller settings. Furthermore, in order to obtain satisfactory dynamic characteristics of the transition process, the absolute value of the error time integral performance index is used as the minimum performance index function of parameter selection, and the square term of the control input is added to the performance index function to prevent excessive controller energy. Finally, it can be observed from the simulation results of the highest value emax of the displacement tracking error, the average value eμ of error, and the standard deviation eσ of error that the performance of the ABC parameters optimized by PSO+CP+ATVC is superior to the manually given ABC parameters. Therefore, this control method significantly improves the stability and speed of the control system. It provides a new research idea for the parameter optimization of controllers.

Design and analysis of high-gain DC–DC chopper topologies for PV-fed BLDC motor drive system

PurposeSwitching power converters for photovoltaic (PV) applications with high gain are rapidly expanding. To obtain better voltage gain, low switch stress, low ripple and cost-effective converters, researchers are developing several topologies.Design/methodology/approachIt was decided to use the particle swarm optimization approach for this system in order to compute the precise PI controller gain parameters under steady state and dynamic changing circumstances. A high-gain q- ZS boost converter is used as an intermittent converter between a PV and brushless direct current (BLDC) motor to attain maximum power point tracking, which also reduces the torque ripples. A MATLAB/Simulink environment has been used to build and test the positive output quadratic boost high gain converters (PQBHGC)-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. The simulation results show that the PQBHGC-3 topology is effective in comparison with other HG cell DC–DC converters in terms of efficiency, reduced ripples, etc. which is most suitable for PV-driven BLDC applications.FindingsThe simulation results have showed that the PQBHGC-3 gives better performance with minimum voltage ripple of 2V and current ripple of 0.4A which eventually reduces the ripples in the torque in a BLDC motor. Also, the efficiency for the suggested PQBHGC-3 for PV-based BLDC applications is the best with 99%.Originality/valueThis study is the first of its kind comparing the different topologies of PQBHGC-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. This study suggests that the PQBHGC-3 topology is most suitable in PV-driven BLDC applications.

Physics-enhanced machine learning-based optimization of tuned mass damper parameters for seismically-excited buildings

Optimal design of tuned mass damper (TMD) parameters should be implemented before device installation to achieve the desired vibration reduction effects for tall and quite slender buildings. Although various optimization algorithms have been developed for searching TMD parameters, there has been very limited exploration of machine learning-based optimization approach in this domain. In this paper, two machine learning-based algorithms are proposed to optimize the natural frequency and damping ratio of a TMD device for seismically excited building. The first algorithm investigates the single-objective optimization of a TMD parameters on a linear building structure. It is based on physics-enhanced generative and adversarial network (GAN) architecture. A generative network randomly generates the primary TMD parameters within the specified parameter ranges, while an adversarial network guides the evolution of parameters to acquire updated parameters, a physical evaluation network assesses the performance of the updated and primary parameters, subsequently providing training data for both the generated network and adversarial network. Furthermore, a second physics-enhanced machine learning algorithm is proposed for the multi-objective optimization of a TMD parameters on a nonlinear building structure under various seismic excitations. The physical evaluation network is extended to consider multiple objective functions, in which the multiple objective functions are integrated through a kind of decomposition scheme, i.e., weighted sum approach. Then, a better set of parameters can be determined and a Pareto optimal solution is obtained. To verify the effectiveness of the two proposed algorithms, single-objective optimization of a TMD parameters for a linear shear-type structure under seismic excitation and a multiple-objectives optimization of a TMD parameters for a seismically excited nonlinear moment-resisting frame (MRF) are conducted and the optimizations are validated by comparing with those by the conventional particle swarm optimization (PSO) approach.

PySubdiv 1.0: open-source geological modeling and reconstruction by non-manifold subdivision surfaces

Abstract. Sealed geological models are commonly used as an input to process simulations, for example, in hydrogeological or geomechanical studies. Creating these meshes often requires tedious manual work, and it is therefore difficult to adjust a once-created model. In this work, we propose a flexible framework to create and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the control mesh and the definition of semi-sharp-crease values, which, in combination, enable the representation of complex structural settings with a low number of control points. We achieve this flexibility through the adaptation of recent algorithms from the field of computer graphics to the specific requirements of geological modeling, specifically the representation of non-manifold topologies and sharp features. We combine the method with a particle swarm optimization (PSO) approach to enable the automatic optimization of vertex position and crease sharpness values. The result of this work is implemented in an open-source software (PySubdiv) for reconstructing geological structures while resulting in a model which is (1) sealed/watertight, (2) controllable with a control mesh and (3) topologically similar to the input geological structure. Also, the reconstructed model may include a lower number of vertices compared to the input geological structure, which results in reducing the cost of modeling and simulation. In addition to enabling a manual adjustment of sealed geological models, the algorithm also provides a method for the integration of explicit surface representations in inverse frameworks and the consideration of uncertainties.

Evaluation and optimization of a horizontal well-triplet for CO2 plume geothermal harvest: Comparison between open and close reservoirs

Depleted oil/gas reservoirs can be used for CO2 sequestration and utilization; CO2 plume geothermal (CPG) production from a CO2 geological storage reservoir has emerged as a viable approach owing to its favorable thermodynamic properties. However, most oil/gas reservoirs are separated by faults into geological blocks, and the impact of these fault boundaries on CPG performance remains unclear. This study evaluated and compared the technical performances of a horizontal well-triplet placement and CPG operation in a thin geothermal reservoir between open and close boundaries. A meta-model simulation–optimization framework was also applied for the economic assessment of both systems. For each boundary system, 200 samples of the horizontal well-triplet design, including injection well overpressure, horizontal perforation length of injection and production wells, and distance between injection and production well, were derived from a 4D-parameter space constrained by particular ranges. These 200 samples of input parameters yielded 200 numerical models for the simulation of CPG operations. A suite of technical metrics, including lifespan, injected, produced, and stored CO2, recovered heat energy, and produced heat flux, were computed from the model simulations for technical performance evaluations. Meta models for economic objectives were developed based on the 200 input–output datasets and used in optimization. A multi-objective particle swarm optimization (MOPSO) approach was utilized to obtain the appropriate optimal designs of the well-triplet for the two boundary systems. The results indicated that performance of both systems was controlled by the well space, which was positively correlated to CO2 storage and total heat recovery but negatively to power plant capacity. High horizontal well lengths and overpressure facilitated CO2 storage, heat recovery, and plant capacity. The open system could generate much more overall profit than the close system owing to CO2 storage income.

Voltage Stability Improvement Using Intelligence Technique

Numerous Artificial Intelligence procedures have been anticipated for reactive power dispatch to maintain voltage stability of the power system. This paper presents particle swarm optimization (PSO) approach for framework boundaries improvement and ideal reactive power dispatch. The transformers tap transformers, Generator exciters, switchable VAR sources/Static VAR compensators (SVC) are utilized as control factors for progression of framework boundaries and voltage stability. The recommended procedure depends on the minimization of total of the squares of L-index value at all load bus. The PSO Algorithm is tested on an IEEE 30 bus test system. The presentation of PSO is compared with conventional technique and results are introduced for illustration purpose. The margin of voltage stability is estimated in terms of system parameters.

Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach

Distribution network operators and planners face a significant challenge in optimizing planning and scheduling strategies to enhance distribution network efficiency. Using improved particle swarm optimization (IPSO), this paper presents an effective method for improving distribution system performance by concurrently deploying remote-controlled sectionalized switches, distributed generation (DG), and optimal network reconfiguration. The proposed optimization problem’s main objectives are to reduce switch costs, maximize reliability, reduce power losses, and enhance voltage profiles. An analytical reliability evaluation is proposed for DG-enhanced reconfigurable distribution systems, considering both switching-only and repairs and switching interruptions. The problem is formulated in the form of a mixed integer nonlinear programming problem, which is known as an NP-hard problem. To solve the problem effectively while improving conventional particle swarm optimization (PSO) exploration and exploitation capabilities, a novel chaotic inertia weight and crossover operation mechanism is developed here. It is demonstrated that IPSO can be applied to both single- and multi-objective optimization problems, where distribution systems’ optimization strategies are considered sequentially and simultaneously. Furthermore, IPSO’s effectiveness is validated and evaluated against well-known state-of-the-art metaheuristic techniques for optimizing IEEE 69-node distribution systems.

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best response for BLDC motor speed. The PID controller in the third method based on neural network also give best reaction on motor speed. Finally, comparison made in speed and torque profiles by using sudden changes in speed and load torque under the three proposed methods. The results show when using first controller the speed rise to 1,526 r.p.m and drop to 1,400 r.p.m at the test conditions. These oscillations will disappear when using the second and third controller.

Two-phase heat transfer microchannel system identification with Particle Swarm Optimization (PSO) approach

The complex behavior of two-phase flow particularly in microchannels can be unpredictable. Experimental measurements are near impossible because of the unavailable compatible assessment equipment. Meanwhile, repeated experiments for reliability of outcomes are costly and involved much time and effort. Environmentally friendly propane is currently being considered as a replacement for hazardous coolants in available refrigeration and air-conditioning systems. This paper reports a system identification (SI) analysis of the collected experimental data of two-phase flow of refrigerant R290 in a microchannel test rig. An ARX model was chosen as the dynamic model, and the modeling of the input–output data was done using a new methodology based on particle swarm optimization (PSO) technique. Measured temperature difference across the microchannel test section and the mass flow rate were the input and output, respectively. The performance of the particle swarm optimization with discoverer (PSOd) was investigated and compared to the original PSO technique. The model was then validated by mean-squared error (MSE). Results demonstrate the advantages of discoverer in PSOd over its standard counterpart with a smaller MSE of 6.2629 × 10−11 and a faster convergence. The SI allows a better prediction of the mass flow rate before any further experiments to obtain the heat transfer coefficient are done. The model provides better management of design of experiments that involve the complex two-phase flow in a microchannel, consequently saving experimental time and cost.

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

The propensity of manufacturers to produce goods at affordable cost, with more accuracy, and at a faster rate force them to search for novel solutions, such as deploying robots in place of people in a sector that can accommodate their needs. Welding is one of the most crucial processes in the automotive industry. This process is time-consuming, subject to error, and demands skilled professionals. The robotic application can improve this area of production and quality. Other industries, such as painting and material handling, can also profit from the use of robots. This work describes the fuzzy DC linear servo controller, which functions as a robotic arm actuator. Robots have been widely employed in most productive sectors in recent years, including assembly plates, welding, tasks at higher temperatures, etc. Controlling a robot accurately is a difficult undertaking as a robot is very nonlinear with many joints that are often organized and unstructured. To carry out the effective task, an effective PID control based on fuzzy logic has been employed together with the method of Particle Swarm Optimization (PSO) approach for the estimate of the parameter. This offline technique determines the lowest number of optimal robotic arm control parameters. To verify the controller design with computer simulation, a comparative assessment of controllers is given by means of a fuzzy surveillance controller with PSO which improves the parameter gain to provide a rapid climb, a smaller overflow, no steady condition error signal, and effective torque control of the robot arm.

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.

FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data

FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data

Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

Autonomous mobile robot global path planning: a prior information-based particle swarm optimization approach

Hybrid GWO-PSO based optimal placement and sizing of multiple PV-DG units for power loss reduction and voltage profile improvement

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.

Application of particle swarm optimization to understand the mechanism of action of allosteric inhibitors of the enzyme HSD17β13

Understanding a drug candidate's mechanism of action is crucial for its further development. However, kinetic schemes are often complex and multi-parametric, especially for proteins in oligomerization equilibria. Here, we demonstrate the use of particle swarm optimization (PSO) as a method to select between different sets of parameters that are too far apart in the parameter space to be found by conventional approaches. PSO is based upon the swarming of birds: each bird in the flock assesses multiple landing spots while atthe same time sharing that information with its neighbors. We applied this approach to the kinetics ofHSD17β13 enzyme inhibitors, which displayed unusually large thermal shifts. Thermal shift data for HSD17β13 indicated that the inhibitor shifted the oligomerization equilibrium toward the dimeric state. Validation of the PSO approach was provided by experimental mass photometry data. These results encourage further exploration of multi-parameter optimization algorithms as tools in drug discovery.

Hybrid Optimization Approach for Improved Efficiency in Permanent Magnet Brushless DC Motor Design: Combining Cauchy Particle Swarm & Moth Flame Methods

This article proposes a novel design strategy for a PMBLDC motor through hybrid optimization, combining Cauchy Particle Swarm Optimization and Moth Flame Optimization approaches to find the optimal set of design parameters that yield the highest efficiency. The motivation is to improve the efficiency of PMBLDC motors, which are widely used in electric cars and renewable energy systems that place a premium on minimal power usage. The proposed work implements the hybrid optimization technique performs conventional single optimization strategies in terms of efficiency improvement. This study contributes in identifying the optimal values for the various design parameters of PMBLDC motors using the hybrid CPSO-MFO approach, which converged to the optimal design parameters. The performance of the PMBLDC motor was evaluated using the optimal design parameters, with an overall power loss of 49.1 W, and an efficiency of 97.88%. The study’s results provide valuable insights for design engineers into the optimal geometrical parameters of PMBLDC motors to achieve higher efficiency and lower power loss. The proposed novel hybrid optimization approach, which is faster and more efficient than conventional single optimization approaches, making it a useful tool for designing high-performance PMBLDC motors.

Optimal Allocation and Assessment of Distributed Generation Units with Multi-Objective Planning in Unbalanced Distribution Networks

—Real-world distribution networks are generally unbalanced due to unbalanced loads and asymmetric line parameters. Proper allocation of distributed generation (DG) units in unbalanced radial distribution networks to extract their maximum potential benefits is still quite difficult. Converter-coupled DG units have individual phase real and reactive power regulating ability and provide voltage unbalance compensation. This article proposes a new technique for the suitable allocation and capacity of converter-based multiple DG units considering the unbalanced operation of distribution networks. The objective function contains power loss minimization, multiphase voltage stability augmentation, and reduction of voltage unbalances among different phases in the system. A complex multi-objective optimization problem has been synthesized with the help of a Fast and Flexible Radial Power Flow (FFRPF) and solved by employing a Weighted aggregation (WA) based particle swarm optimization (PSO) approach. The performance of the proposed technique is validated on 19-bus, 25-bus, and 34-bus unbalanced radial distribution networks that consider various power factor modes of the converter-coupled DG units. The analysis of the simulation results indicates a significant enhancement in the power delivery efficiency in all the unbalanced test systems

An Aerial Robotics Investigation into the Stability, Coordination, and Movement of Strategies for Directing Swarm and Formation of Autonomous MAVs and Diverse Groups of Driverless Vehicles (UGVs)

This study will discuss the matter of movement communication and preparation of tight configurations of land & flying robots. Remotely Operated Cars (UGVs) and Unmanned Aerial Vehicles (UAVs), in specific Micro Aerial Vehicles (MAVs), would be used to fix circumstances where a creation of UGVs and UAVs, in specific Micro Aerial Vehicles (MAVs), should counteract their velocity and direction to finish a mission of traffic sequence to a targeted area. The motion planning and stabilisation strategy given here is a useful tool for deploying closely collaborating robot teams including both outdoor and indoor settings. The installation of large groups of Micro Aerial Vehicles (MAVs) in a legitimate (indoor and outdoor) environment without the use of auxiliary positioning applications (such as Vicon or GPS) is indeed a natural development in the area of autonomously flying systems. Stability, control, and trajectory planning techniques for guiding swarm or configurations of unmanned MAVs, as well as diverse groups with Unmanned Ground Vehicles (UGVs) operating alongside MAVs, will be discussed in greater detail. These approaches discussed all are designed for the use of inter squads in true complex scenarios even without necessity for worldwide translation or motion capture systems, as they are predicated on board optical comparative localisation of single MAVs. This multi - objective optimisation being an enabler for the introduction of swarming of tiny autonomous drones beyond the labs with equipment for precise robot positioning. Model Predictive Control (MPC) is being used to address a formations to goal territory issue, and the form drive idea is based on a simulated approach. The Particle swarm optimization approach is utilised for digital leader trajectories planning, as well as control and stabilisation of follows (MAVs and UGVs). The proposed technique could be tested in the future using a range of simulation and practical tests.

Inverse Kinematics for Serial Robot Manipulators by Particle Swarm Optimization and POSIX Threads Implementation

Inverse kinematics is a fundamental problem in manipulator robotics: a set of joint angles must be calculated so that the robot arm can be manipulated to the corresponding desired end effector position and orientation (also known as “pose”). Traditional solution techniques include analytical kinematics solvers, which provide the closed-form expressions for the joint positions as functions of the end-effector pose. When analytical inverse kinematics solvers are not possible due to the manipulator structure, numerical methods such as Newton–Raphson or Jacobian inverse can be used to achieve the task, but at a much slower speed due, to the iterative nature of the computation. Recent swarm intelligence technology has also contributed to manipulator inverse kinematics solutions. In this paper, the use of the Particle Swarm Optimization (PSO) approach in solving the inverse kinematics problem is investigated for the general serial robotic manipulators. Many of the reviewed robotic manipulator inverse kinematics solvers using swarm intelligence only deal with end effector position and not its orientation. Our PSO approach provides the convergence of a complete end-effector pose and will be demonstrated using the Baxter Research Robot, which has two seven-joint arms, although the method is applicable to any general serial robotic manipulator. For computational efficiency, the inverse kinematic calculations were implemented in parallel using Portable Operating Interface (POSIX) threads to take advantage of the independent swarm particle dynamics.