Particle Swarm Optimization and it's Applications in Electromagnetics
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It is a Seminar presented by Swapnil Gaul on January 18,2012 at IIT Kharagpur as a part of M.Tech Part I curriculum.
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Particle swarm optimization
Particle swarm optimization (PSO) is an evolutionary computation technique for optimizing problems. It initializes a population of random solutions and searches for optima by updating generations. Each potential solution, called a particle, tracks its best solution and the overall best solution to change its velocity and position in search of better solutions. The algorithm involves initializing particles with random positions and velocities, then updating velocities and positions iteratively based on the particles' local best solution and the global best solution until termination criteria are met. PSO has advantages of being simple, quick, and effective at locating good solutions.
PSO.ppt
The document proposes using particle swarm optimization (PSO) for supervised hyperspectral band selection to reduce data dimensionality before classification. It describes existing band selection approaches, how PSO can be applied to band selection, and reports classification results on two hyperspectral datasets that show PSO band selection improves SVM classification accuracy over other methods.
Particle Swarm Optimization
Particle swarm optimization is a population-based stochastic optimization technique inspired by bird flocking or fish schooling. It works by having a population of candidate solutions, called particles, and moving these particles around in the search space according to simple mathematical formulae over the particle's position and velocity. Each particle keeps track of its coordinates in the problem space which are associated with the best solution that particle has achieved so far. The main idea is that hope flies along with the flock.
PSO and Its application in Engineering
The document discusses Particle Swarm Optimization (PSO) algorithms and their application in engineering design optimization. It provides an overview of optimization problems and algorithms. PSO is introduced as an evolutionary computational technique inspired by animal social behavior that can be used to find global optimization solutions. The document outlines the basic steps of the PSO algorithm and how it works by updating particle velocities and positions to track the best solutions. Examples of applications to model fitting and inductor design optimization are provided.
Particle Swarm optimization
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence. It summarizes that PSO was developed in 1995 and can be applied to various search and optimization problems. PSO works by having a swarm of particles that communicate locally to find the best solution within a search space, balancing exploration and exploitation.
Particle Swarm Optimization: The Algorithm and Its Applications
A simple and useful presentation about Particle Swarm Optimization: The Algorithm and Its Applications.
Practical Swarm Optimization (PSO)
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
Particle swarm optimization
The document discusses particle swarm optimization (PSO), which is a population-based optimization technique where multiple candidate solutions called particles fly through the problem search space looking for the optimal position. Each particle adjusts its position based on its own experience and the experience of neighboring particles. The procedure for implementing PSO involves initializing particles with random positions and velocities, evaluating each particle, updating particles' velocities and positions based on personal and global best experiences, and repeating until a stopping criterion is met. The document also discusses modifications to basic PSO such as limiting maximum velocity, adding an inertia weight, using a constriction factor, features of PSO, and strategies for selecting PSO parameters.
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Particle swarm optimization
Particle swarm optimization (PSO) is an evolutionary computation technique for optimizing problems. It initializes a population of random solutions and searches for optima by updating generations. Each potential solution, called a particle, tracks its best solution and the overall best solution to change its velocity and position in search of better solutions. The algorithm involves initializing particles with random positions and velocities, then updating velocities and positions iteratively based on the particles' local best solution and the global best solution until termination criteria are met. PSO has advantages of being simple, quick, and effective at locating good solutions.
PSO.ppt
The document proposes using particle swarm optimization (PSO) for supervised hyperspectral band selection to reduce data dimensionality before classification. It describes existing band selection approaches, how PSO can be applied to band selection, and reports classification results on two hyperspectral datasets that show PSO band selection improves SVM classification accuracy over other methods.
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Particle swarm optimization is a population-based stochastic optimization technique inspired by bird flocking or fish schooling. It works by having a population of candidate solutions, called particles, and moving these particles around in the search space according to simple mathematical formulae over the particle's position and velocity. Each particle keeps track of its coordinates in the problem space which are associated with the best solution that particle has achieved so far. The main idea is that hope flies along with the flock.
PSO and Its application in Engineering
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Particle Swarm optimization
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence. It summarizes that PSO was developed in 1995 and can be applied to various search and optimization problems. PSO works by having a swarm of particles that communicate locally to find the best solution within a search space, balancing exploration and exploitation.
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A simple and useful presentation about Particle Swarm Optimization: The Algorithm and Its Applications.
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The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
Particle swarm optimization
The document discusses particle swarm optimization (PSO), which is a population-based optimization technique where multiple candidate solutions called particles fly through the problem search space looking for the optimal position. Each particle adjusts its position based on its own experience and the experience of neighboring particles. The procedure for implementing PSO involves initializing particles with random positions and velocities, evaluating each particle, updating particles' velocities and positions based on personal and global best experiences, and repeating until a stopping criterion is met. The document also discusses modifications to basic PSO such as limiting maximum velocity, adding an inertia weight, using a constriction factor, features of PSO, and strategies for selecting PSO parameters.
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This document discusses using particle swarm optimization (PSO) for feature selection in text categorization. It provides an introduction to PSO, explaining how it was inspired by bird flocking behavior. The document outlines the PSO algorithm, parameters, and concepts like particle velocity and position updating. It also discusses feature selection techniques like filter and wrapper methods and compares different feature utility measures that can be used.
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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.
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Particle Swarm Optimization is an algorithm inspired by swarm behavior in nature that was invented by James Kennedy and Russell Eberhart. It is used to find the minimum value of a function by tracking the global best solution and each particle's self best solution. The algorithm works by initializing a random set of solution vectors and updating their positions based on their own experience and the experience of neighboring particles, getting closer to the optimal solution over many iterations similar to the way a flock moves together toward food sources.
Particle Swarm Optimization (A Circuit Optimization Problem)
Particle swarm optimization is used to optimize circuit parameters to meet a specific design goal. The algorithm models the movement of bird flocks. It defines a fitness function that is maximized to find the optimal values of four circuit parameters (R1, R5, R8, V3) such that the power from voltage source V1 is 10% of the power from V3. The circuit equations are set up and solved using mesh current analysis. PSO runs for multiple iterations, updating particle velocities and positions to find the parameter values that maximize fitness.
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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.
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The document proposes developing an artificial intelligence-based stock trading system using particle swarm optimization. It finds that using 30 neural networks with a 100-day moving time interval to select the top 3 stock picks daily based on the highest recommendations produces the most stable and profitable results. The system uses swarm intelligence to search for the globally best-performing neural network each day to make trading decisions.
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HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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