A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
A brief introduction on the principles of particle swarm optimizaton by Rajorshi Mukherjee. This presentation has been compiled from various sources (not my own work) and proper references have been made in the bibliography section for further reading. This presentation was made as a presentation for submission for our college subject Soft Computing.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
A brief introduction on the principles of particle swarm optimizaton by Rajorshi Mukherjee. This presentation has been compiled from various sources (not my own work) and proper references have been made in the bibliography section for further reading. This presentation was made as a presentation for submission for our college subject Soft Computing.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
SA is a global optimization technique.
It distinguishes between different local optima.
It is a memory less algorithm & the algorithm does not use any information gathered during the search.
SA is motivated by an analogy to annealing in solids.
& it is an iterative improvement algorithm.
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
The identification of traversable terrain within an environment is essential for the development of autonomous robotic networks capable of operating over a wide area within a rapidly evolving environment. We propose the usage of many robots with fixed formations to be able to move through an environment and provide the necessary odometry to map out the region. We design and implement an algorithm for a group of four Kilobots, robots that are capable of broadcasting communication, motion, distance sensing, but not odometry to walk together in a straight line with a square formation. Our algorithm is able to provide consistent motion that far outstrips the capability of a single robot. Specifically, we find that algorithms that try to fix the angles between constituent robots perform well in maintaining a prescribed bearing, but tend to drift in the long run. However, algorithms that try to move first to keep their lateral edges parallel take a while to equilibriate, but tend to hold their bearing and formation for longer without any type of global error correction. We hope that our studies can serve as a springboard for future work on odometry with formation control.
A REVIEW OF PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHMIAEME Publication
Particle swarm optimization (PSO) is a population-based stochastic optimization technique that is inspired by the intelligent collective behaviour of certain animals, such as flocks of birds or schools of fish. It has undergone numerous improvements since its debut in 1995. As academics became more familiar with the technique, they produced additional versions aimed at different demands, created new applications in a variety of fields, published theoretical analyses of the impacts of various factors, and offered other variants of the algorithm. This paper discusses the PSO's origins and background, as well as its theory analysis. Then, we examine the current state of research and application in algorithm structure, parameter selection, topological structure, discrete and parallel PSO algorithms, multi-objective optimization PSO, and engineering applications. Finally, existing difficulties are discussed, and new study directions are proposed.
MPPT for Photovoltaic System Using Multi-objective Improved Particle Swarm Op...Nooria Sukmaningtyas
Making full use of abundant renewable solar energy through the development of photovoltaic (PV)
technology is an effective means to solve the problems such as difficulty in electricity supply and energy
shortages in remote rural areas. In order to improve the electricity generating efficiency of PV cells, it is
necessary to track the maximum power point of PV array, which is difficult to make under partially shaded
conditions due to the odds of the appearance of two or more local maximum power points., In this paper, a
control algorithm of maximum power point tracking (MPPT) based on improved particle swarm optimization
(IPSO) algorithm is presented for PV systems. Firstly, the current in maximum power point is searched
with the IPSO algorithm, and then the real maximum power point is tracked through controlling the output
current of PV array. The MPPT method based on IPSO algorithm is established and simulated with Matlab
/ Simulink, and meanwhile, the comparison between IPSO MPPT algorithm and traditional MPPT algorithm
is also performed in this paper. It is proved through simulation and experimental results that the IPSO
algorithm has good performances and very fast response even to partial shaded PV modules, , which
ensures the stability of PV system.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
Reliable and accurate estimation of software has always been a matter of concern for industry and
academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all
types of datasets and environments. Since the motive of estimation model is to minimize the gap between
actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization,
Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of
COCOMO Model. The performance of these techniques has been analysed by established performance
measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR) projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
PARTICLE SWARM INTELLIGENCE: A PARTICLE SWARM OPTIMIZER WITH ENHANCED GLOBAL ...Hennegrolsch
A new particle swarm optimizer is presented. The new optimizer for static optimization problems incorporates superior global search characteristics and guarantees final convergence.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
This is inspired from Tom Mitchell's book on Machine Learning. You can achieve a bit exact implementation of the back propagation algorithm if you follow the code in this.
A simple client-server application in java in which a client sends a message to a server and the server tries to be funny by sending back a funny response.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
7. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
ANT COLONY OPTIMIZATION
A valuable technique for mathematical optimization.
Takes inspiration from swarming behavior of birds, animals
or insects.
Useful for discrete and continuous optimization problems.
In telecommunications: Routing and load balancing.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
8. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
BIOLOGICAL INSPIRATION
Inception – early 90’s.
Proposed by Kennedy and Eberhardt.
Social psychologist and electrical engineer.
Based on observation of bird flocks searching for corn.
Birds are social animals.
Birds are also driven by the goal of community survival
rather than being focused on survival of the individuals.
Bird’s foraging behavior: How birds swarm together as they
search for food.
Convergence: How the whole swarm converges to good
corn fields (optimum solutions)
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
9. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
SWARMING BEHAVIOR OF BIRDS
When searching for food birds:
As a single bird finds a good corn source.
Other birds try to converge to it so that they can also grab
some food.
Which other birds: They are the neighboring birds.
Who are the neighbors: Neighborhood functions.
Birds drift together probabilistically, meaning sometimes
they move closer and sometimes they lurch away.
Benefit: Better exploration of corn fields for good corn.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
10. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
BENEFITS
Indirect communication between birds enables them to
converge to better food sources.
Random probabilistic search enables them to find better,
globally optimal, food sources as opposed to substandard,
locally optimal, ones.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
11. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
BASIC IDEAS
A number of simple entities – particles – are placed in the
search space of some problem or function.
Each particle evaluates the objective function at its current
location.
Each particle determines its movement through the search
space by:
1 Combining some aspect of the history of its own current
and best locations with those of one or more members of
the swarm.
2 And with some random perturbations.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
12. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
THE ALGORITHM
The next iteration takes place when all particles have been
moved.
Eventually the swarm as a whole is likely to move close to
an optimum of the fitness function.
Like a flock of birds foraging for food.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
13. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
DATA STRUCTURES
Each individual is composed of three D-dimensional
vectors.
D is the dimensionality of the search space.
The vectors are: The current position xi , the previous best
position pi, and the velocity vi .
The current position xi can be considered as a set of
coordinates describing a point in space.
On each iteration of the algorithm, the current position is
evaluated as a problem solution.
If that position is better than any that has been found so far,
then the coordinates are stored in the second vector, pi.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
14. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
USING DATA STRUCTURES
The value of the best function result so far is stored in a
variable that can be called pbesti (for previous best)., for
comparison on latter iterations.
The objective, of course, is to keep finding better positions
and updating pi and pbesti.
New points are chosen by adding vi coordinates to xi , and
the algorithm operates by adjusting vi .
vi is effectively the step size.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
15. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
WHY SWARMING IS IMPORTANT?
The particle swarm is more than just a collection of
particles.
A particle itself has almost no power to solve any problem.
Progress occurs only when particles interact.
Problem solving is a population-wide phenomenon.
It emerges from the individual behaviors of the particles
through their interactions.
In any case, populations are organized according to some
sort of communication structure or topology.
This is often thought of as a social network.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
16. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
SWARM TOPOLOGY
The topology typically consists of bidirectional edges
connecting pairs of particles.
So that if j is in i’s neighborhood, i is also in j’s.
Each particle communicates with some other particles.
And is affected by the best point found by any member of
its topological neighborhood.
This is just the vector pi for that best neighbor.
We denote this with pg.
The potential kinds of population ""social networks" are
hugely varied.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
17. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
SWARM TOPOLOGY
In practice certain types have been used more frequently.
Velocity is iteratively adjusted to allow the particles to
oscillate.
Topologies can be static and dynamic depending on the
problem.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
18. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
THE ALGORITHM – PSEUDOCODE I
1 Initialize a population array of particles with random
positions and velocities and D-dimensions in the search
space.
2 Begin loop:
3 For each particle, evaluate the desired optimization fitness
function in D variables.
4 Compare particleÕs fitness evaluation with its pbesti. If
current value is better than pbesti , then set pbesti equal to
the current value, and pi equal to the current location xi in
D-dimensional space.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
19. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
THE ALGORITHM – PSEUDOCODE II
5 Identify the particle in the neighborhood with the best
success so far, and assign its index to the variable g.
6 Change the velocity and position of the particle.
vi ← vi + U(0, φ1) ⊗ (pi − xi ) + U(0, φ2) ⊗ (pg − xi ) (1)
xi ← xi + vi (2)
7 If a criterion is met (usually a sufficiently good fitness or a
maximum number of iterations), exit loop.
8 End loop
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
20. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
THE ALGORITHM – PSEUDOCODE III
Where:
U(0, φ1) represents a vector of random numbers uniformly
distributed in [0, φi ] which is randomly generated at each
iteration and for each particle.
⊗ is component-wise multiplication.
In the original version of PSO, each component of vi is
kept within the range [−Vmax , +Vmax ]
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
22. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
APPLICATIONS
Image and video analysis.
Design and restructuring of electricity networks and load
dispatching.
Control applications.
Applications in electronics and electromagnetics.
Antenna design.
Power generation and power systems.
Scheduling.
Design applications.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
23. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
APPLICATIONS
Design and optimization of communication networks.
Biological, medical and pharmaceutical.
Clustering, classification and data mining.
Fuzzy and neuro-fuzzy systems and control.
Signal processing.
Neural networks.
Combinatorial optimization problems.
Robotics.
Prediction and forecasting.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
24. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
APPLICATIONS
Modeling.
Detection and diagnosis of faults and recovery from them.
Sensors and sensor networks.
Applications in computer graphics and visualization.
Design or optimization of engines and electrical motors.
Applications in metallurgy.
Music generation and games.
Security and military applications.
Finance and economics.
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications
25. Introduction
Biological Inspiration
The Algorithm
Applications
Conclusions
CONCLUSIONS
A great algorithm.
Bio-inspiration is the key.
Emulation of real bird swarming behavior..
Easy to comprehend.
Many variants.
Many applications.
Problem formulation is the real trick.
Inspiration (reference): Particle Swarm Optimization,
Riccardo Poli, James Kennedy and Tim Blackwell
Muhammad Adil Raja Particle Swarm Optimization: Algorithm and Applications