scholarly journals Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Hong Chen ◽  
Ying-ping Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Experiments ◽  
Particle Interaction ◽  
Particle Swarm ◽  
Convergence Time ◽  
Statistical Interpretation ◽  
Time Analysis ◽  
Swarm Optimization ◽  
The Social ◽  
Theoretical Results

We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.

A Comprehensive Review of Particle Swarm Optimization

2016 ◽  
Vol 23 ◽  
pp. 141-161 ◽  
Author(s):  
Ben Bright Benuwa ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Sefakor Awurama Adabunu
Keyword(s):  
Particle Swarm Optimization ◽  
Social Relations ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimization Method ◽  
Future Research ◽  
Swarm Optimization ◽  
Research Directions ◽  
The Social ◽  
Low Constraint

Particle swarm optimization (PSO) is a heuristic global optimization method. PSO was motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and the ability to adapt various dynamic environments, makes PSO one of the most important swarm intelligence algorithms and ostensibly the most commonly used optimization technique. This survey presents a comprehensive investigation of PSO and in particular, a proposed theoretical framework to improve its implementation. We hope that this survey would be beneficial to researchers studying PSO algorithms and would also serve as the substratum for future research in the study area, particularly those pursuing their career in artificial intelligence. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed.

scholarly journals An enhanced particle swarm optimization algorithm

2019 ◽  
Vol 9 (6) ◽  
pp. 4904
Author(s):  
Wameedh Riyadh Abdul-Adheem
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
The Social ◽  
Basic Particle ◽  
Stochastic Optimization Algorithm ◽  
Enhanced Particle Swarm Optimization

<p>In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.</p>

The Novel Parameter Selection of Particle Swarm Optimization

2012 ◽  
Vol 479-481 ◽  
pp. 344-347
Author(s):  
Zhuo Li ◽  
Xue Luo Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Swarm Optimization ◽  
Intelligent Technique ◽  
The Social ◽  
Scale Optimization ◽  
Artificial Intelligent Technique

Particle Swarm Optimization (PSO) is a novel artificial intelligent technique proposed by Eberhart and Kennedy which is a type of Swarm Intelligence. PSO is simulated as population-based stochastic optimization influenced by the social behavior of bird flocks. In past decades, more and more researcher has been targeting to improve the original PSO for solving various problems and it has great potential to be done further. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.

Particle Swarms

2011 ◽  
pp. 1174-1203
Author(s):  
James Kennedy
Keyword(s):  
Particle Swarm Optimization ◽  
Social Network ◽  
Topological Space ◽  
Particle Swarm ◽  
Social Psychological ◽  
Swarm Optimization ◽  
Particle Swarms ◽  
Social Network Structure ◽  
The Social ◽  
Evaluative Space

Particle swarm optimization is a computer paradigm that is based on human social influence and cognition. Candidate problem solutions are randomly initialized, and improvements are found through interactions among them. Social-psychological aspects of the algorithm are described, followed by implementation details. The particle swarm operates in three kinds of spaces, namely a topological space comprising the “social network” structure of the population, a parameter space of problem variables, and a one-dimensional evaluative space. Variations in the algorithm are described, and finally it is compared to evolutionary computation models.

scholarly journals Estimating one-diode-PV model using autonomous groups particle swarm optimization

2021 ◽  
Vol 10 (1) ◽  
pp. 166
Author(s):  
Mohammad AlShabi ◽  
Chaouki Ghenai ◽  
Maamar Bettayeb ◽  
Fahad Faraz Ahmad
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Mean Squared Error ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Maximum Absolute Error ◽  
Swarm Optimization ◽  
The Social ◽  
The One

In this paper, the one-diode model of a photovoltaic PV solar cell (PVSC) is estimated for an experimental characteristic curves data by using a recently proposed version of the Particle Swarm Optimization (PSO) algorithm, which is known as the Autonomous Groups Particles Swarm Optimization (PSOAG). This meta-heuristic algorithm is used to identify the model of the PVSC. The PSOAG divides the particles into groups and then, uses different functions to tune the social and cognitive parameters of these groups. This is done to show the individuals’ diversity inside the swarm. Although, these individuals do their duties as part of the society, they are not similar in terms of intelligence and ability. By using these groups, the performance of the PSO is improved in terms of convergence rate and escaping the local minima/maxima. Six versions of PSOAG algorithms were developed in this work. Therefore, nine versions of PSOAG, including these six algorithms and three newly developed PSOAG reported previously, will be used in this research to cover more social’s behaviors. The results are compared to the original PSO and other versions of PSO like conventional and Asymmetric Time-varying Accelerated Coefficient PSOs, and the improved PSO. The result shows that the proposed methods improve the performance by up to 14% in terms of root mean squared error and maximum absolute error, and by up to 20% in term of convergence rate, when these were compared to the best results obtained from the other algorithms.

Order-2 Stability Analysis of Particle Swarm Optimization

2015 ◽  
Vol 23 (2) ◽  
pp. 187-216 ◽  
Author(s):  
Qunfeng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Stability Analysis ◽  
Numerical Experiments ◽  
Particle Swarm ◽  
Stable Region ◽  
Swarm Optimization ◽  
Parameter Combination ◽  
Best Parameter ◽  
Definition Of ◽  
Better Than

Several stability analyses and stable regions of particle swarm optimization (PSO) have been proposed before. The assumption of stagnation and different definitions of stability are adopted in these analyses. In this paper, the order-2 stability of PSO is analyzed based on a weak stagnation assumption. A new definition of stability is proposed and an order-2 stable region is obtained. Several existing stable analyses for canonical PSO are compared, especially their definitions of stability and the corresponding stable regions. It is shown that the classical stagnation assumption is too strict and not necessary. Moreover, among all these definitions of stability, it is shown that our definition requires the weakest conditions, and additional conditions bring no benefit. Finally, numerical experiments are reported to show that the obtained stable region is meaningful. A new parameter combination of PSO is also shown to be good, even better than some known best parameter combinations.

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