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.

Particle Swarms

2005 ◽  
pp. 235-269 ◽  
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 Hierarchical winner-take-all particle swarm optimization social network for neural model fitting

2016 ◽  
Vol 42 (1) ◽  
pp. 71-85 ◽  
Author(s):  
Brandon S. Coventry ◽  
Aravindakshan Parthasarathy ◽  
Alexandra L. Sommer ◽  
Edward L. Bartlett
Keyword(s):  
Particle Swarm Optimization ◽  
Social Network ◽  
Model Fitting ◽  
Particle Swarm ◽  
Neural Model ◽  
Swarm Optimization ◽  
Winner Take All ◽  
Take All

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 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.

scholarly journals Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

2017 ◽  
Author(s):  
Adithya Sagar ◽  
Rachel LeCover ◽  
Christine Shoemaker ◽  
Jeffrey Varner
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Coagulation Cascade ◽  
Benchmark Problems ◽  
Test Functions ◽  
Swarm Optimization ◽  
Particle Swarms

AbstractBackgroundMathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.ResultsWe tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed trials with function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade.ConclusionsDOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org.

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>

scholarly journals Inverse Parameter Identification Technique Using PSO Algorithm Applied to Geotechnical Modeling

2008 ◽  
Vol 2008 ◽  
pp. 1-14 ◽  
Author(s):  
Joerg Meier ◽  
Winfried Schaedler ◽  
Lisa Borgatti ◽  
Alessandro Corsini ◽  
Tom Schanz
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Soil Mechanics ◽  
Constitutive Relations ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Material Behavior ◽  
Swarm Optimization ◽  
Particle Swarms ◽  
Identification Technique

This paper presents a concept for the application of particle swarm optimization in geotechnical engineering. For the calculation of deformations in soil or rock, numerical simulations based on continuum methods are widely used. The material behavior is modeled using constitutive relations that require sets of material parameters to be specified. We present an inverse parameter identification technique, based on statistical analyses and a particle swarm optimization algorithm, to be used in the calibration process of geomechanical models. Its application is demonstrated with typical examples from the fields of soil mechanics and engineering geology. The results for two different laboratory tests and a natural slope clearly show that particle swarms are an efficient and fast tool for finding improved parameter sets to represent the measured reference data.

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.

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