Advances in Particle Swarm Optimization
Particle swarm optimization can be defined as a computational method that is used to optimize a problem by iteratively trying to improve a candidate solution with respect to a given measure of quality. It is deployed to solve a problem by having a population of candidate solutions and moving them around in the search-space in accordance with simple mathematical formulae over the particle's position and velocity. Particle swarm optimization can search very large spaces of candidate solutions because it is metaheuristic and does not make any assumptions about the problem being optimized. There are various variants of particle swamp optimization such as hybridization, simplifications, multi-objective optimization, and binary, discrete, and combinational particle swamp optimization. This book elucidates the concepts and innovative models around prospective developments in relation to particle swarm optimization. Different approaches, evaluations, methodologies, and advanced studies on this topic have been included in it. This book will serve as a reference to a broad spectrum of readers.
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Advances in Particle Swarm Optimization
Particle swarm optimization can be defined as a computational method that is used to optimize a problem by iteratively trying to improve a candidate solution with respect to a given measure of quality. It is deployed to solve a problem by having a population of candidate solutions and moving them around in the search-space in accordance with simple mathematical formulae over the particle's position and velocity. Particle swarm optimization can search very large spaces of candidate solutions because it is metaheuristic and does not make any assumptions about the problem being optimized. There are various variants of particle swamp optimization such as hybridization, simplifications, multi-objective optimization, and binary, discrete, and combinational particle swamp optimization. This book elucidates the concepts and innovative models around prospective developments in relation to particle swarm optimization. Different approaches, evaluations, methodologies, and advanced studies on this topic have been included in it. This book will serve as a reference to a broad spectrum of readers.
153.95 In Stock
Advances in Particle Swarm Optimization

Advances in Particle Swarm Optimization

Advances in Particle Swarm Optimization

Advances in Particle Swarm Optimization

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Overview

Particle swarm optimization can be defined as a computational method that is used to optimize a problem by iteratively trying to improve a candidate solution with respect to a given measure of quality. It is deployed to solve a problem by having a population of candidate solutions and moving them around in the search-space in accordance with simple mathematical formulae over the particle's position and velocity. Particle swarm optimization can search very large spaces of candidate solutions because it is metaheuristic and does not make any assumptions about the problem being optimized. There are various variants of particle swamp optimization such as hybridization, simplifications, multi-objective optimization, and binary, discrete, and combinational particle swamp optimization. This book elucidates the concepts and innovative models around prospective developments in relation to particle swarm optimization. Different approaches, evaluations, methodologies, and advanced studies on this topic have been included in it. This book will serve as a reference to a broad spectrum of readers.

Product Details

ISBN-13: 9781639890248
Publisher: States Academic Press
Publication date: 03/01/2022
Pages: 242
Product dimensions: 6.00(w) x 9.00(h) x 0.56(d)
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