SlideShare ist ein Scribd-Unternehmen logo
1 von 23
Downloaden Sie, um offline zu lesen
Particle Swarm
Optimization
(PSO)
Introduction
 Many difficulties such as multi-
modality, dimensionality and
differentiability are associated with the
optimization of large-scale problems.
 Traditional techniques such as
steepest decent, linear programing
and dynamic programing generally fail
to solve such large-scale problems
especially with nonlinear objective
functions.
Introduction…
 Traditional techniques often fail to
solve optimization problems that have
many local optima.
 To overcome these problems, there is
a need to develop more powerful
optimization techniques.
Introduction…
 Some of the well-known population-
based optimization techniques are:
 Genetic Algorithms (GA)
 Artificial Immune Algorithms (AIA)
 Ant Colony Optimization (ACO)
 Particle Swarm Optimization (PSO)
 Bacteria Foraging Optimization (BFO)
 Artificial Bee Colony (ABC)
 Biogeography-Based Optimization (BBO)
Etc.
Particle Swarm Optimization
(PSO)
 Particle swarm optimization (PSO) is
an evolutionary computation technique
developed by Kennedy and Eberhart.
 It exhibits common evolutionary
computation attributes including
initialization with a population of
random solutions and searching for
optima by updating generations.
Concept
 A Simulation of a simplified social
system.
 The original intent was to graphically
simulate the graceful but
unpredictable choreography of a bird
flock.
 Each particle keeps track of its
coordinates in the problem space,
which are associated with the best
solution (fitness) it has achieved so
How it works ??
 PSO is initialized with a group of
random particles (solutions) and then
 Searches for optima by updating
generations.
 Potential solutions, called particles,
are then ‘‘flown’’ through the problem
space by following the current
optimum particles.
How it Works ??
 Each particle keeps track of its
coordinates in the problem space, which
are associated with the best solution
(fitness) it has achieved so far.
 This value is called ‘pBest’.
 Another "best" value that is tracked by
the particle swarm optimizer is the best
value obtained so far by any particle in
the population.
 This second best value is a global best
and called “gbest”.
How it works ??
 The particle swarm optimization
concept consists of, at each step,
changing the velocity (i.e.
accelerating) of each particle toward
its ‘pBest’ and ‘gBest’ locations (global
version of PSO).
PSO Algorithm (General)
Searches Hyperspace of Problem for
Optimum
 Define problem to search
 How many dimensions?
 Solution criteria?
 Initialize Population
 Random initial positions
 Random initial velocities
 Determine Best Position
 Global Best Position
 Personal Best Position
 Update Velocity and Position Equations
The step-by-step
implementation
Step 1:
 Initialize PSO parameters which are
necessary for the algorithm.
 population size which indicates the
number of individuals,
 number of generations necessary for the
termination criterion,
 cognitive constant, social constant,
 variation of inertia weight, maximum
velocity,
 number of design variables and
respective ranges for the design
variables.
Step 2:
 Generate random population equal to
the population size specified.
 Each population member contains the
value of all the design variables. This
value of design variable is randomly
generated in between the design
variable range specified.
 population means the group of birds
(particles) which represents the set of
solutions.
Step 3:
 Obtain the values of the objective function for
all the population members.
 For the first iteration, value of objective
function indicates the pBest for the respective
particle in the solution.
 Identify the particle with best objective
function value which identifies as gBest.
 If the problem is a constrained optimization
problem, then a specific approach such as
static penalty, dynamic penalty and adaptive
penalty is used to convert the constrained
optimization problem into the unconstrained
optimization problem.
Step 4:
 Update the velocity of each particle
and Check for the maximum velocity.
 If the velocity obtained exceeds the
maximum velocity,
 then reduce the existing velocity to the
maximum velocity.
Step 5:
 Update the position of the particles,
 Check all the design variables for the
upper and lower limits.
Step 6:
 Obtain the value of objective function
for all the particles.
 The new solution replaces the pBest if
it has better function value.
 Identify the gBest from the population.
 Update the value of inertia weight if
required.
Step 7:
 Best obtained results are saved using
elitism.
 All elite members are not modified
using crossover and mutation
operators but can be replaced if better
solutions are obtained in any iteration.
Step 8:
 Repeat the steps (from step 4) until
the specified number of generations or
termination criterion is reached.
Advantages
 PSO is based on the intelligence. It can
be applied into both scientific research
and engineering use.
 PSO have no overlapping and mutation
calculation.
 The search can be carried out by the
speed of the particle. During the
development of several generations, only
the most optimist particle can transmit
information onto the other particles, and
the speed of the researching is very fast.
Advantages…
 The calculation in PSO is very simple.
Compared with the other developing
calculations, it occupies the bigger
optimization ability and it can be
completed easily.
 PSO adopts the real number code,
and it is decided directly by the
solution. The number of the dimension
is equal to the constant of the solution.
Disadvantages
 The method easily suffers from the
partial optimism, which causes the less
exact at the regulation of its speed and
the direction.
 The method can not work out the
problems of scattering and
 The method can not work out the
problems of non-coordinate system,
such as the solution to the energy field
and the moving rules of the particles in
the energy field
Thank You !!

Weitere ähnliche Inhalte

Was ist angesagt?

Particle Swarm Optimization
Particle Swarm OptimizationParticle Swarm Optimization
Particle Swarm OptimizationQasimRehman
 
Particle swarm optimization
Particle swarm optimization Particle swarm optimization
Particle swarm optimization Ahmed Fouad Ali
 
Particle Swarm Optimization - PSO
Particle Swarm Optimization - PSOParticle Swarm Optimization - PSO
Particle Swarm Optimization - PSOMohamed Talaat
 
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimizationAbhishek Agrawal
 
Ant colony optimization
Ant colony optimizationAnt colony optimization
Ant colony optimizationJoy Dutta
 
Ant Colony Optimization - ACO
Ant Colony Optimization - ACOAnt Colony Optimization - ACO
Ant Colony Optimization - ACOMohamed Talaat
 
Nature-Inspired Optimization Algorithms
Nature-Inspired Optimization Algorithms Nature-Inspired Optimization Algorithms
Nature-Inspired Optimization Algorithms Xin-She Yang
 
Ant Colony Optimization presentation
Ant Colony Optimization presentationAnt Colony Optimization presentation
Ant Colony Optimization presentationPartha Das
 
Metaheuristic Algorithms: A Critical Analysis
Metaheuristic Algorithms: A Critical AnalysisMetaheuristic Algorithms: A Critical Analysis
Metaheuristic Algorithms: A Critical AnalysisXin-She Yang
 
Artificial Bee Colony algorithm
Artificial Bee Colony algorithmArtificial Bee Colony algorithm
Artificial Bee Colony algorithmAhmed Fouad Ali
 
Ant colony optimization
Ant colony optimizationAnt colony optimization
Ant colony optimizationvk1dadhich
 
PSO and Its application in Engineering
PSO and Its application in EngineeringPSO and Its application in Engineering
PSO and Its application in EngineeringPrince Jain
 
Bat algorithm explained. slides ppt pptx
Bat algorithm explained. slides ppt pptxBat algorithm explained. slides ppt pptx
Bat algorithm explained. slides ppt pptxMahdi Atawneh
 
Optimization and particle swarm optimization (O & PSO)
Optimization and particle swarm optimization (O & PSO) Optimization and particle swarm optimization (O & PSO)
Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
 
Swarm intelligence pso and aco
Swarm intelligence pso and acoSwarm intelligence pso and aco
Swarm intelligence pso and acosatish561
 
Ant Colony Optimization
Ant Colony OptimizationAnt Colony Optimization
Ant Colony OptimizationPratik Poddar
 
Ant colony optimization
Ant colony optimizationAnt colony optimization
Ant colony optimizationMeenakshi Devi
 

Was ist angesagt? (20)

Particle Swarm Optimization
Particle Swarm OptimizationParticle Swarm Optimization
Particle Swarm Optimization
 
Particle swarm optimization
Particle swarm optimization Particle swarm optimization
Particle swarm optimization
 
Particle Swarm Optimization - PSO
Particle Swarm Optimization - PSOParticle Swarm Optimization - PSO
Particle Swarm Optimization - PSO
 
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimization
 
Ant colony optimization
Ant colony optimizationAnt colony optimization
Ant colony optimization
 
Ant Colony Optimization - ACO
Ant Colony Optimization - ACOAnt Colony Optimization - ACO
Ant Colony Optimization - ACO
 
Nature-Inspired Optimization Algorithms
Nature-Inspired Optimization Algorithms Nature-Inspired Optimization Algorithms
Nature-Inspired Optimization Algorithms
 
Ant Colony Optimization presentation
Ant Colony Optimization presentationAnt Colony Optimization presentation
Ant Colony Optimization presentation
 
Metaheuristic Algorithms: A Critical Analysis
Metaheuristic Algorithms: A Critical AnalysisMetaheuristic Algorithms: A Critical Analysis
Metaheuristic Algorithms: A Critical Analysis
 
Metaheuristics
MetaheuristicsMetaheuristics
Metaheuristics
 
Artificial Bee Colony algorithm
Artificial Bee Colony algorithmArtificial Bee Colony algorithm
Artificial Bee Colony algorithm
 
Genetic Algorithms
Genetic AlgorithmsGenetic Algorithms
Genetic Algorithms
 
Ant colony optimization
Ant colony optimizationAnt colony optimization
Ant colony optimization
 
PSO and Its application in Engineering
PSO and Its application in EngineeringPSO and Its application in Engineering
PSO and Its application in Engineering
 
Bat algorithm explained. slides ppt pptx
Bat algorithm explained. slides ppt pptxBat algorithm explained. slides ppt pptx
Bat algorithm explained. slides ppt pptx
 
Optimization and particle swarm optimization (O & PSO)
Optimization and particle swarm optimization (O & PSO) Optimization and particle swarm optimization (O & PSO)
Optimization and particle swarm optimization (O & PSO)
 
PSO
PSOPSO
PSO
 
Swarm intelligence pso and aco
Swarm intelligence pso and acoSwarm intelligence pso and aco
Swarm intelligence pso and aco
 
Ant Colony Optimization
Ant Colony OptimizationAnt Colony Optimization
Ant Colony Optimization
 
Ant colony optimization
Ant colony optimizationAnt colony optimization
Ant colony optimization
 

Ähnlich wie Particle swarm optimization

Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...IOSR Journals
 
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...Ahmed Gamal Abdel Gawad
 
Artificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path PlanningArtificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path Planningiosrjce
 
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
 
Comparison between the genetic algorithms optimization and particle swarm opt...
Comparison between the genetic algorithms optimization and particle swarm opt...Comparison between the genetic algorithms optimization and particle swarm opt...
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
 
3-SAT Problem A New Memetic-PSO Algorithm.pdf
3-SAT Problem  A New Memetic-PSO Algorithm.pdf3-SAT Problem  A New Memetic-PSO Algorithm.pdf
3-SAT Problem A New Memetic-PSO Algorithm.pdfSandra Valenzuela
 
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
 
Particle swarm optimization (PSO) ppt presentation
Particle swarm optimization (PSO) ppt presentationParticle swarm optimization (PSO) ppt presentation
Particle swarm optimization (PSO) ppt presentationLatestShorts
 
Biology-Derived Algorithms in Engineering Optimization
Biology-Derived Algorithms in Engineering OptimizationBiology-Derived Algorithms in Engineering Optimization
Biology-Derived Algorithms in Engineering OptimizationXin-She Yang
 
IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...
IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...
IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...IRJET Journal
 
Efficient evaluation of flatness error from Coordinate Measurement Data using...
Efficient evaluation of flatness error from Coordinate Measurement Data using...Efficient evaluation of flatness error from Coordinate Measurement Data using...
Efficient evaluation of flatness error from Coordinate Measurement Data using...Ali Shahed
 
Two-Stage Eagle Strategy with Differential Evolution
Two-Stage Eagle Strategy with Differential EvolutionTwo-Stage Eagle Strategy with Differential Evolution
Two-Stage Eagle Strategy with Differential EvolutionXin-She Yang
 
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer  Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer ijsc
 
Software Effort Estimation Using Particle Swarm Optimization with Inertia Weight
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightSoftware Effort Estimation Using Particle Swarm Optimization with Inertia Weight
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightWaqas Tariq
 

Ähnlich wie Particle swarm optimization (20)

M017127578
M017127578M017127578
M017127578
 
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
 
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
 
Pso notes
Pso notesPso notes
Pso notes
 
Artificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path PlanningArtificial Intelligence in Robot Path Planning
Artificial Intelligence in Robot Path Planning
 
T01732115119
T01732115119T01732115119
T01732115119
 
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...
 
Comparison between the genetic algorithms optimization and particle swarm opt...
Comparison between the genetic algorithms optimization and particle swarm opt...Comparison between the genetic algorithms optimization and particle swarm opt...
Comparison between the genetic algorithms optimization and particle swarm opt...
 
50120140504022
5012014050402250120140504022
50120140504022
 
04 1 evolution
04 1 evolution04 1 evolution
04 1 evolution
 
3-SAT Problem A New Memetic-PSO Algorithm.pdf
3-SAT Problem  A New Memetic-PSO Algorithm.pdf3-SAT Problem  A New Memetic-PSO Algorithm.pdf
3-SAT Problem A New Memetic-PSO Algorithm.pdf
 
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...
 
Particle swarm optimization (PSO) ppt presentation
Particle swarm optimization (PSO) ppt presentationParticle swarm optimization (PSO) ppt presentation
Particle swarm optimization (PSO) ppt presentation
 
Biology-Derived Algorithms in Engineering Optimization
Biology-Derived Algorithms in Engineering OptimizationBiology-Derived Algorithms in Engineering Optimization
Biology-Derived Algorithms in Engineering Optimization
 
1 sati
1 sati1 sati
1 sati
 
IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...
IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...
IRJET- PSO based PID Controller for Bidirectional Inductive Power Transfer Sy...
 
Efficient evaluation of flatness error from Coordinate Measurement Data using...
Efficient evaluation of flatness error from Coordinate Measurement Data using...Efficient evaluation of flatness error from Coordinate Measurement Data using...
Efficient evaluation of flatness error from Coordinate Measurement Data using...
 
Two-Stage Eagle Strategy with Differential Evolution
Two-Stage Eagle Strategy with Differential EvolutionTwo-Stage Eagle Strategy with Differential Evolution
Two-Stage Eagle Strategy with Differential Evolution
 
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer  Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer
 
Software Effort Estimation Using Particle Swarm Optimization with Inertia Weight
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightSoftware Effort Estimation Using Particle Swarm Optimization with Inertia Weight
Software Effort Estimation Using Particle Swarm Optimization with Inertia Weight
 

Kürzlich hochgeladen

Mohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxMohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxKISHAN KUMAR
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Amil baba
 
ASME BPVC 2023 Section I para leer y entender
ASME BPVC 2023 Section I para leer y entenderASME BPVC 2023 Section I para leer y entender
ASME BPVC 2023 Section I para leer y entenderjuancarlos286641
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Sean Meyn
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technologyabdulkadirmukarram03
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxNaveenVerma126
 
Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...sahb78428
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging systemgokuldongala
 
Gender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 ProjectGender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 Projectreemakb03
 
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchrohitcse52
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecTrupti Shiralkar, CISSP
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationMohsinKhanA
 
IT3401-WEB ESSENTIALS PRESENTATIONS.pptx
IT3401-WEB ESSENTIALS PRESENTATIONS.pptxIT3401-WEB ESSENTIALS PRESENTATIONS.pptx
IT3401-WEB ESSENTIALS PRESENTATIONS.pptxSAJITHABANUS
 
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratoryدليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide LaboratoryBahzad5
 
Engineering Mechanics Chapter 5 Equilibrium of a Rigid Body
Engineering Mechanics  Chapter 5  Equilibrium of a Rigid BodyEngineering Mechanics  Chapter 5  Equilibrium of a Rigid Body
Engineering Mechanics Chapter 5 Equilibrium of a Rigid BodyAhmadHajasad2
 
Modelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovationsModelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovationsYusuf Yıldız
 
Design of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptxDesign of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptxYogeshKumarKJMIT
 

Kürzlich hochgeladen (20)

Mohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxMohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptx
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
 
ASME BPVC 2023 Section I para leer y entender
ASME BPVC 2023 Section I para leer y entenderASME BPVC 2023 Section I para leer y entender
ASME BPVC 2023 Section I para leer y entender
 
Litature Review: Research Paper work for Engineering
Litature Review: Research Paper work for EngineeringLitature Review: Research Paper work for Engineering
Litature Review: Research Paper work for Engineering
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technology
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
 
Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...
 
Lecture 2 .pdf
Lecture 2                           .pdfLecture 2                           .pdf
Lecture 2 .pdf
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging system
 
Gender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 ProjectGender Bias in Engineer, Honors 203 Project
Gender Bias in Engineer, Honors 203 Project
 
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
 
Lecture 2 .pptx
Lecture 2                            .pptxLecture 2                            .pptx
Lecture 2 .pptx
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software Simulation
 
IT3401-WEB ESSENTIALS PRESENTATIONS.pptx
IT3401-WEB ESSENTIALS PRESENTATIONS.pptxIT3401-WEB ESSENTIALS PRESENTATIONS.pptx
IT3401-WEB ESSENTIALS PRESENTATIONS.pptx
 
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratoryدليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
 
Engineering Mechanics Chapter 5 Equilibrium of a Rigid Body
Engineering Mechanics  Chapter 5  Equilibrium of a Rigid BodyEngineering Mechanics  Chapter 5  Equilibrium of a Rigid Body
Engineering Mechanics Chapter 5 Equilibrium of a Rigid Body
 
Modelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovationsModelling Guide for Timber Structures - FPInnovations
Modelling Guide for Timber Structures - FPInnovations
 
Design of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptxDesign of Clutches and Brakes in Design of Machine Elements.pptx
Design of Clutches and Brakes in Design of Machine Elements.pptx
 

Particle swarm optimization

  • 2. Introduction  Many difficulties such as multi- modality, dimensionality and differentiability are associated with the optimization of large-scale problems.  Traditional techniques such as steepest decent, linear programing and dynamic programing generally fail to solve such large-scale problems especially with nonlinear objective functions.
  • 3. Introduction…  Traditional techniques often fail to solve optimization problems that have many local optima.  To overcome these problems, there is a need to develop more powerful optimization techniques.
  • 4. Introduction…  Some of the well-known population- based optimization techniques are:  Genetic Algorithms (GA)  Artificial Immune Algorithms (AIA)  Ant Colony Optimization (ACO)  Particle Swarm Optimization (PSO)  Bacteria Foraging Optimization (BFO)  Artificial Bee Colony (ABC)  Biogeography-Based Optimization (BBO) Etc.
  • 5. Particle Swarm Optimization (PSO)  Particle swarm optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart.  It exhibits common evolutionary computation attributes including initialization with a population of random solutions and searching for optima by updating generations.
  • 6. Concept  A Simulation of a simplified social system.  The original intent was to graphically simulate the graceful but unpredictable choreography of a bird flock.  Each particle keeps track of its coordinates in the problem space, which are associated with the best solution (fitness) it has achieved so
  • 7. How it works ??  PSO is initialized with a group of random particles (solutions) and then  Searches for optima by updating generations.  Potential solutions, called particles, are then ‘‘flown’’ through the problem space by following the current optimum particles.
  • 8. How it Works ??  Each particle keeps track of its coordinates in the problem space, which are associated with the best solution (fitness) it has achieved so far.  This value is called ‘pBest’.  Another "best" value that is tracked by the particle swarm optimizer is the best value obtained so far by any particle in the population.  This second best value is a global best and called “gbest”.
  • 9. How it works ??  The particle swarm optimization concept consists of, at each step, changing the velocity (i.e. accelerating) of each particle toward its ‘pBest’ and ‘gBest’ locations (global version of PSO).
  • 10. PSO Algorithm (General) Searches Hyperspace of Problem for Optimum  Define problem to search  How many dimensions?  Solution criteria?  Initialize Population  Random initial positions  Random initial velocities  Determine Best Position  Global Best Position  Personal Best Position  Update Velocity and Position Equations
  • 12. Step 1:  Initialize PSO parameters which are necessary for the algorithm.  population size which indicates the number of individuals,  number of generations necessary for the termination criterion,  cognitive constant, social constant,  variation of inertia weight, maximum velocity,  number of design variables and respective ranges for the design variables.
  • 13. Step 2:  Generate random population equal to the population size specified.  Each population member contains the value of all the design variables. This value of design variable is randomly generated in between the design variable range specified.  population means the group of birds (particles) which represents the set of solutions.
  • 14. Step 3:  Obtain the values of the objective function for all the population members.  For the first iteration, value of objective function indicates the pBest for the respective particle in the solution.  Identify the particle with best objective function value which identifies as gBest.  If the problem is a constrained optimization problem, then a specific approach such as static penalty, dynamic penalty and adaptive penalty is used to convert the constrained optimization problem into the unconstrained optimization problem.
  • 15. Step 4:  Update the velocity of each particle and Check for the maximum velocity.  If the velocity obtained exceeds the maximum velocity,  then reduce the existing velocity to the maximum velocity.
  • 16. Step 5:  Update the position of the particles,  Check all the design variables for the upper and lower limits.
  • 17. Step 6:  Obtain the value of objective function for all the particles.  The new solution replaces the pBest if it has better function value.  Identify the gBest from the population.  Update the value of inertia weight if required.
  • 18. Step 7:  Best obtained results are saved using elitism.  All elite members are not modified using crossover and mutation operators but can be replaced if better solutions are obtained in any iteration.
  • 19. Step 8:  Repeat the steps (from step 4) until the specified number of generations or termination criterion is reached.
  • 20. Advantages  PSO is based on the intelligence. It can be applied into both scientific research and engineering use.  PSO have no overlapping and mutation calculation.  The search can be carried out by the speed of the particle. During the development of several generations, only the most optimist particle can transmit information onto the other particles, and the speed of the researching is very fast.
  • 21. Advantages…  The calculation in PSO is very simple. Compared with the other developing calculations, it occupies the bigger optimization ability and it can be completed easily.  PSO adopts the real number code, and it is decided directly by the solution. The number of the dimension is equal to the constant of the solution.
  • 22. Disadvantages  The method easily suffers from the partial optimism, which causes the less exact at the regulation of its speed and the direction.  The method can not work out the problems of scattering and  The method can not work out the problems of non-coordinate system, such as the solution to the energy field and the moving rules of the particles in the energy field