scholarly journals The Generalized PSO: A New Door to PSO Evolution

2008 ◽  
Vol 2008 ◽  
pp. 1-15 ◽  
Author(s):  
J. L. Fernández Martínez ◽  
E. García Gonzalo
Keyword(s):  
Inverse Problems ◽  
Global Minimum ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Step ◽  
Continuous Version ◽  
Stability Regions ◽  
Griewank Function ◽  
The Stability ◽  
Exploration Task

A generalized form of the particle swarm optimization (PSO) algorithm is presented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a time step different than the unit. Generalized continuous particle swarm optimizations are compared in terms of attenuation and oscillation. The deterministic and stochastic stability regions and their respective asymptotic velocities of convergence are analyzed as a function of the time step and the GPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect of stochasticity on the stability of trajectories. The stability regions for the second-, third-, and fourth-order moments depend on inertia, local, and global accelerations and the time step and are inside of the deterministic stability region for the same time step. We prove that stability regions are the same under stagnation and with a moving center of attraction. Properties of the second-order moments variance and covariance serve to propose some promising parameter sets. High variance and temporal uncorrelation improve the exploration task while solving ill-posed inverse problems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments using well-known benchmark functions with two types of ill-posedness commonly found in inverse problems: the Rosenbrock and the “elongated” DeJong functions (global minimum located in a very flat area), and the Griewank function (global minimum surrounded by multiple minima). Numerical simulations support the results provided by theoretical analysis. Based on these results, two variants of Generalized PSO algorithm are proposed, improving the convergence and the exploration task while solving real applications of inverse problems.

scholarly journals Transient Stability Assessment by Coordinated Control of SVC and TCSC with Particle Swarm Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 2506-2510
Keyword(s):  
Particle Swarm Optimization ◽  
Transient Stability ◽  
Control Strategies ◽  
Particle Swarm ◽  
Coordinated Control ◽  
Pso Algorithm ◽  
Small Time ◽  
Local Power ◽  
Swarm Optimization ◽  
The Stability

This paper aims on improving the stability of a 9 bus power system under fault condition using coordination of FACTS device. Flexible A.C. transmission system (FACTS) can be regulated reliable with faster output and can improve local power grid status with control with appropriate control strategies in very small time period. Based on that, a particle swarm optimization (PSO) algorithm was executed, to design the coordinated parameter of static VAR compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC). Simulation is performed on WSCC 9-bus system in MATLAB software. When 3 phase fault is applied near to generator, frequency and rotor angle changes accordingly. With coordinated control of FACTS devices with PSO has implemented both, near to its normal condition. PSO performed in this paper was structured on identifying the values of L and C of SVC and TCSC, for superior coordination.

scholarly journals Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

10.5772/45692 ◽  
2011 ◽  
Vol 8 (5) ◽  
pp. 57 ◽  
Author(s):  
Haifa Mehdi ◽  
Olfa Boubaker
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fine Tuning ◽  
Fast Method ◽  
Constrained Motion ◽  
Circular Trajectory ◽  
Swarm Optimization ◽  
Robotic Arms ◽  
The Stability

This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov-based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO) algorithm. For designing the PSO method, different index performances are considered in both joint and Cartesian spaces. A 3DOF manipulator constrained to a circular trajectory is finally used to validate the performances of the proposed approach. The simulation results show the stability and the performances of the proposed approach.

scholarly journals Enhancement transient stability of power system using UPFC with M-PSO

2020 ◽  
Vol 17 (1) ◽  
pp. 61
Author(s):  
Rashid H. AL-Rubayi ◽  
Luay G. Ibrahim
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Transient Stability ◽  
Electrical Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Simulation Results ◽  
The Stability

<span>During the last few decades, electrical power demand enlarged significantly whereas power production and transmission expansions have been brutally restricted because of restricted resources as well as ecological constraints. Consequently, many transmission lines have been profoundly loading, so the stability of power system became a Limiting factor for transferring electrical power. Therefore, maintaining a secure and stable operation of electric power networks is deemed an important and challenging issue. Transient stability of a power system has been gained considerable attention from researchers due to its importance. The FACTs devices that provide opportunities to control the power and damping oscillations are used. Therefore, this paper sheds light on the modified particle swarm optimization (M-PSO) algorithm is used such in the paper to discover the design optimal the Proportional Integral controller (PI-C) parameters that improve the stability the Multi-Machine Power System (MMPS) with Unified Power Flow Controller (UPFC). Performance the power system under event of fault is investigating by utilizes the proposed two strategies to simulate the operational characteristics of power system by the UPFC using: first, the conventional (PI-C) based on Particle Swarm Optimization (PI-C-PSO); secondly, (PI-C) based on modified Particle Swarm Optimization (PI-C-M-PSO) algorithm. The simulation results show the behavior of power system with and without UPFC, that the proposed (PI-C-M-PSO) technicality has enhanced response the system compared for other techniques, that since it gives undershoot and over-shoot previously existence minimized in the transitions, it has a ripple lower. Matlab package has been employed to implement this study. The simulation results show that the transient stability of the respective system enhanced considerably with this technique.</span>

scholarly journals Optimising monthly tilt angles of solar panels using particle swarm optimisation algorithm

2021 ◽  
Vol 23 (1) ◽  
pp. 75
Author(s):  
Namruta S. Kanianthara ◽  
Swee Peng Ang ◽  
Ashraf Fathi Khalil Sulayman ◽  
Zainidi bin Hj. Abd. Hamid
Keyword(s):  
Tilt Angle ◽  
Global Minimum ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Optimisation ◽  
Solar Panels ◽  
Abc Algorithm ◽  
Optimisation Algorithm ◽  
Brunei Darussalam ◽  
Particle Swarm Optimisation Algorithm

This paper presents an intelligent computational method using the PSO (particle swarm optimisation) algorithm to determine the optimum tilt angle of solar panels in PV systems. The objective of the paper is to assess the performance of this method against conventional methods of determining the optimum tilt angle. The method presented here can be used to determine the optimum tilt angle at any location around the world. In this paper, it was applied to Brunei Darussalam, and succeeded in computing monthly optimum tilt angles, ranging from 34.7ᵒ in December to -26.7ᵒ in September. Results showed that changing the tilt angle every month, as determined by the PSO algorithm, increased annual yield by: (i) 5.94%, compared to keeping it fixed at 0ᵒ, (ii) 8.65%, compared to Lunde’s method and (iii) 17.31%, compared to Duffie and Beckman’s method. Benchmark test functions were used to compare and evaluate the performance of the PSO algorithm with the artificial bee colony (ABC) algorithm, another metaheuristic algorithm. The tests revealed that the PSO algorithm outperformed the ABC algorithm, exhibiting lower root mean square error and standard deviation, better convergence to the global minimum, more accurate location of the global minimum, and faster execution times.

scholarly journals Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bowen Liu ◽  
Zhenwei Wang ◽  
Xiaoyong Zhong
Keyword(s):  
Numerical Simulation ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Rock Slope ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Saturated Rock ◽  
The Stability

With the continuous popularization and development of highway traffic in mountainous areas, the number of rock slopes is also increasing. In order to improve the stability of rock slope and reduce the harm caused by slope slip, this paper carries out numerical simulation of rock slope sliding based on particle swarm optimization algorithm. Firstly, this paper combines the differential evolution algorithm and simplex method to improve the global and local search ability of particle swarm optimization (PSO) algorithm and analyzes the performance of the algorithm. ABAQUS software is used to simulate rock slope sliding, the finite element method is used to analyze the stability of rock slope, and LS-DYNA program is used to simulate rockfall impact rock slope. During the numerical simulation, the improved algorithm is used to analyze all the data. Experimental data show that the improved PSO algorithm converges after nearly 100 iterations and the convergence speed and optimization accuracy are high. In the numerical simulation, the average failure probability of the left and right sides of the main section at the top, middle, and foot of the slope is 0.0820 and 0.0723, 0.0772 and 0.0492, and 0.0837 and 0.0677, respectively, indicating that the overall instability probability of the left side of the rock slope is higher than that of the right side. The rock slope with the same direction through joint is mainly affected by the joint at the toe of the slope, the rock slope with reverse through joint is mainly affected by the joint in the slope, and the sliding occurs from the middle to both ends. In addition, with the increase of the size and height of rockfall, the total energy of rock slope is also increasing, and the possibility and degree of rock slope sliding are higher. This shows that the improved particle swarm optimization algorithm can effectively analyze some factors affecting slope slip in numerical simulation of saturated rock slope slip.

scholarly journals Research on steering control performance of electric forklift with steer by wire

2021 ◽  
Vol 12 ◽  
pp. 1
Author(s):  
Chuan Feng
Keyword(s):  
High Speed ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Steering Control ◽  
Sideslip Angle ◽  
Cargo Handling ◽  
Locally Optimal Solution ◽  
The Stability ◽  
Improved Pso

Forklift plays an important role in cargo handling in the warehouse; therefore, it is necessary to ensure the stability of the forklift when turning to guarantee the safety of transportation. In this study, the particle swarm optimization (PSO) algorithm was improved by a genetic algorithm (GA), and the parameters of the proportion, integration, and differentiation (PID) controller were calculated using the improved algorithm for forklift steering control. Then simulation experiments were carried out using MATLAB. The results showed that the convergence speed of the improved PSO algorithm was faster than that of GA, and its adaptive value after convergence stability was significantly lower than that of the PSO algorithm; whether it was low-speed or high-speed steering, the three algorithms responded to the steering signal quickly; the yaw velocity and sideslip angle of the forklift steering under the improved PSO algorithm were more suitable for stable steering, and the increase of the steering speed would increase the yaw velocity. The novelty of this paper is that the traditional PSO algorithm is improved by GA and the particle swarm jumps out of the locally optimal solution through the crossover and mutation operations.

scholarly journals Steady state particle swarm

2019 ◽  
Vol 5 ◽  
pp. e202 ◽  
Author(s):  
Carlos M. Fernandes ◽  
Nuno Fachada ◽  
Juan-Julián Merelo ◽  
Agostinho C. Rosa
Keyword(s):  
Steady State ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Step ◽  
Solution Vector ◽  
Interacting Species ◽  
Dynamic Update ◽  
Update Strategy

This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.

scholarly journals Parameter Estimation in Rainfall-Runoff Modelling Using Distributed Versions of Particle Swarm Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Michala Jakubcová ◽  
Petr Máca ◽  
Pavel Pech
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Problems ◽  
Hydrological Model ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Estimation Of Parameters ◽  
Swarm Optimization

The presented paper provides the analysis of selected versions of the particle swarm optimization (PSO) algorithm. The tested versions of the PSO were combined with the shuffling mechanism, which splits the model population into complexes and performs distributed PSO optimization. One of them is a new proposed PSO modification, APartW, which enhances the global exploration and local exploitation in the parametric space during the optimization process through the new updating mechanism applied on the PSO inertia weight. The performances of four selected PSO methods were tested on 11 benchmark optimization problems, which were prepared for the special session on single-objective real-parameter optimization CEC 2005. The results confirm that the tested new APartW PSO variant is comparable with other existing distributed PSO versions, AdaptW and LinTimeVarW. The distributed PSO versions were developed for finding the solution of inverse problems related to the estimation of parameters of hydrological model Bilan. The results of the case study, made on the selected set of 30 catchments obtained from MOPEX database, show that tested distributed PSO versions provide suitable estimates of Bilan model parameters and thus can be used for solving related inverse problems during the calibration process of studied water balance hydrological model.

COMPARISON OF PARTICLE SWARM AND EVOLUTIONARY PROGRAMMING AS THE GLOBAL CONFORMATION OPTIMIZER OF CLUSTERS

2005 ◽  
Vol 16 (04) ◽  
pp. 591-606 ◽  
Author(s):  
MASAO IWAMATSU
Keyword(s):  
Global Minimum ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evolutionary Programming ◽  
General Purpose ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Self Adaptation ◽  
The One

The particle swarm optimization (PSO) algorithm and two variants of the evolutionary programming (EP) are applied to the several function optimization problems and the conformation optimization of atomic clusters to check the performance of these algorithms as a general-purpose optimizer. It was found that the PSO is superior to the EP though the PSO is not equipped with the mechanism of self-adaptation of search strategies of the EP. The PSO cannot find the global minimum for the atomic cluster but can find it for similar multi-modal benchmark functions of the same size. The size of the cluster which can be handled by the PSO and the EP is limited, and is similar to the one amenable to the popular simulated annealing. The result for benchmark functions only serves as an indication of the performance of the algorithm.

scholarly journals Adoption of computer particle swarm optimization algorithm under thermodynamic motion mechanism

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2707-2715
Author(s):  
Qizhong Li ◽  
Yizheng Yue ◽  
Zhongqi Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Test Function ◽  
Motion Mechanism ◽  
Local Optimal Solution ◽  
The Stability ◽  
Better Than

In order to improve the stability and sensitivity of particle swarm optimization (PSO) algorithm and to solve the problem of premature convergence, in this re-search, a computer PSO algorithm based on thermodynamic motion mechanism is proposed based on the principle of thermodynamic motion mechanism. Firstly, the thermodynamic motion phenomenon, the diffusion law in kinematics and the standard PSO algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized PSO algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analysed by setting the test function. The results show that among the five test functions, the computer PSO algorithm based on thermodynamic motion mechanism has a higher probability of jumping out of the local optimal solution. Its robustness and stability are much better than standard PSO algorithms. The evolution ability of the computer PSO algorithm based on thermodynamic motion mechanism is better than that of the standard PSO algorithm. The standard PSO algorithm is superior because it is based on thermodynamic motion mechanism. The research in this paper can provide good guidance for improving the performance of PSO algorithm.

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