Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

Numerical function optimization by conditionalized PSO algorithm

2020 ◽  
Vol 39 (3) ◽  
pp. 3275-3295
Author(s):  
Yin Tianhe ◽  
Mohammad Reza Mahmoudi ◽  
Sultan Noman Qasem ◽  
Bui Anh Tuan ◽  
Kim-Hung Pho
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Numerical Function ◽  
Problem Space ◽  
Swarm Optimization ◽  
Speed Up ◽  
Suboptimal Solution

A lot of research has been directed to the new optimizers that can find a suboptimal solution for any optimization problem named as heuristic black-box optimizers. They can find the suboptimal solutions of an optimization problem much faster than the mathematical programming methods (if they find them at all). Particle swarm optimization (PSO) is an example of this type. In this paper, a new modified PSO has been proposed. The proposed PSO incorporates conditional learning behavior among birds into the PSO algorithm. Indeed, the particles, little by little, learn how they should behave in some similar conditions. The proposed method is named Conditionalized Particle Swarm Optimization (CoPSO). The problem space is first divided into a set of subspaces in CoPSO. In CoPSO, any particle inside a subspace will be inclined towards its best experienced location if the particles in its subspace have low diversity; otherwise, it will be inclined towards the global best location. The particles also learn to speed-up in the non-valuable subspaces and to speed-down in the valuable subspaces. The performance of CoPSO has been compared with the state-of-the-art methods on a set of standard benchmark functions.

A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization

2015 ◽  
Vol 740 ◽  
pp. 401-404
Author(s):  
Yun Zhi Li ◽  
Quan Yuan ◽  
Yang Zhao ◽  
Qian Hui Gang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Swarm Algorithm

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

2014 ◽  
Vol 971-973 ◽  
pp. 1242-1246
Author(s):  
Tie Jun Chen ◽  
Yan Ling Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

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 Combination of Intermittent Search Strategy and an Improve Particle Swarm Optimization algorithm (IPSO) for damage detection of steel frame

2021 ◽  
Vol 16 (59) ◽  
pp. 141-152
Author(s):  
Cuong Le Thanh ◽  
Thanh Sang-To ◽  
Hoang-Le Hoang-Le ◽  
Tran-Thanh Danh ◽  
Samir Khatir ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Search Speed ◽  
Eagle Strategy

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads to promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously. In contrast, Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search locally. ES is easy to deal with its problem via drastic support of Levy flight. As known, the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two problems: 1) avoiding local optimization, and 2) obtaining more accurate results. The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES for some problems and between experiment and FEM to demonstrate its effectiveness. Natural frequencies are used in the objective function to solve this optimization problem. The results show that the combination of IPSO and ES is quite effective.

scholarly journals Train Operation Strategy Optimization Based on a Double-Population Genetic Particle Swarm Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2518
Author(s):  
Kaiwei Liu ◽  
Xingcheng Wang ◽  
Zhihui Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Population Genetic ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Parallel Structure ◽  
Operation Strategy ◽  
Swarm Optimization ◽  
Train Operation ◽  
Simulation Results ◽  
Double Population

Train operation strategy optimization is a multi-objective optimization problem affected by multiple conditions and parameters, and it is difficult to solve it by using general optimization methods. In this paper, the parallel structure and double-population strategy are used to improve the general optimization algorithm. One population evolves by genetic algorithm (GA), and the other population evolves by particle swarm optimization (PSO). In order to make these two populations complement each other, an immigrant strategy is proposed, which can give full play to the overall advantages of parallel structure. In addition, GA and PSO is also improved, respectively. For GA, its convergence speed is improved by adjusting the selection pressure adaptively based on the current iteration number. Elite retention strategy (ERS) is introduced into GA, so that the best individual in each iteration can be saved and enter the next iteration process. In addition, the opposition-based learning (OBL) can produce the opposition population to maintain the diversity of the population and avoid the algorithm falling into local convergence as much as possible. For PSO, linear decreasing inertia weight (LDIW) is presented to better balance the global search ability and local search ability. Both MATLAB simulation results and hardware-in-the-loop (HIL) simulation results show that the proposed double-population genetic particle swarm optimization (DP-GAPSO) algorithm can solve the train operation strategy optimization problem quickly and effectively.

scholarly journals Control of a Robotic Swarm Formation to Track a Dynamic Target with Communication Constraints: Analysis and Simulation

2021 ◽  
Vol 11 (7) ◽  
pp. 3179
Author(s):  
Charles Coquet ◽  
Andreas Arnold ◽  
Pierre-Jean Bouvet
Keyword(s):  
Particle Swarm Optimization ◽  
Initial Conditions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Communication Constraints ◽  
Swarm Optimization ◽  
Simplifying Assumptions ◽  
Robotic Swarm ◽  
And Control

We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the Particle Swarm Optimization (PSO) algorithm for function optimization. Four parameters drive LCPSO—the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF (Artificial Potential Field), we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 850-851 ◽  
pp. 809-812
Author(s):  
Hong Mei Ni ◽  
Wei Gang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Swarm Behavior ◽  
Peak Function ◽  
Improved Pso

Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.

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