Evolutionary algorithms | Metaheuristics
In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is originally attributed to Kennedy, Eberhart and Shi and was first intended for simulating social behaviour, as a stylized representation of the movement of organisms in a bird flock or fish school. The algorithm was simplified and it was observed to be performing optimization. The book by Kennedy and Eberhart describes many philosophical aspects of PSO and swarm intelligence. An extensive survey of PSO applications is made by Poli. Recently, a comprehensive review on theoretical and experimental works on PSO has been published by Bonyadi and Michalewicz. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. Also, PSO does not use the gradient of the problem being optimized, which means PSO does not require that the optimization problem be differentiable as is required by classic optimization methods such as gradient descent and quasi-newton methods. However, metaheuristics such as PSO do not guarantee an optimal solution is ever found. (Wikipedia).
Particle Swarm Optimization (PSO) - Part 1: Introduction
This video is about Particle Swarm Optimization (PSO) - Part 1: Introduction
From playlist Optimization
Particle Swarm Optimization - Part 2: Global Best PSO
This video is about Particle Swarm Optimization - Part 2: Global Best PSO
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Particle Swarm Optimization - Part 3: Local Best PSO
This video is about Particle Swarm Optimization - Part 3: Local Best PSO
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Particle Physics 1: Introduction
Part 1 of a series: covering introduction to Quantum Field Theory, creation and annihilation operators, fields and particles.
From playlist Particle Physics
Ant Colony Optimization - Part 1: Introduction
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From playlist Optimization
Particle Swarm Optimization - Part 4: Velocity Components
This video is about Particle Swarm Optimization - Part 4: Velocity Components
From playlist Optimization
Learn how to determine when a particle is at rest using a calculator
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-01-20 Discussion lead: Maja Maher Discussion facilitator(s): Susan Shu Chang
From playlist Architecture Tuning
Public Lecture 2 - Andrea Cavagna “The seventh Starling: the Wonders of Collective Animal...
Andrea Cavagna “The seventh Starling: the Wonders of Collective Animal Behaviour” The wonderful evolutions of the great flocks of birds across the skies of our cities, often in aerial duel with a falcon, are both a fascinating sight, and a scientific mystery. How does the flock keep its s
From playlist T1-2015 : Disordered systems, random spatial processes and some applications
Challenges in Source Parameter Estimation in GW Astronomy by Rajesh Nayak
20 March 2017 to 25 March 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru This joint program is co-sponsored by ICTS and SAMSI (as part of the SAMSI yearlong program on Astronomy; ASTRO). The primary goal of this program is to further enrich the international collaboration in the area
From playlist Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy
Determine when a particle is increasing
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
How to determine when a particle is moving to the left and right
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
Solving Optimization Problems with MATLAB | Master Class with Loren Shure
In this session, you will learn about the different tools available for optimization in MATLAB. We demonstrate how you can use Optimization Toolbox™ and Global Optimization Toolbox to solve a wide variety of optimization problems. You will learn best practices for setting up and solving op
From playlist MATLAB and Simulink Livestreams
The Statistical Physics of Flocks and Swarms by Irene Giardina
DISCUSSION MEETING : CELEBRATING THE SCIENCE OF GIORGIO PARISI (ONLINE) ORGANIZERS : Chandan Dasgupta (ICTS-TIFR, India), Abhishek Dhar (ICTS-TIFR, India), Smarajit Karmakar (TIFR-Hyderabad, India) and Samriddhi Sankar Ray (ICTS-TIFR, India) DATE : 15 December 2021 to 17 December 2021 VE
From playlist Celebrating the Science of Giorgio Parisi (ONLINE)
Learn how to determine the greatest speed from a velocity graph
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
Particle Swarm Optimization - Part 5: Veclocity Clamping
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