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Particle Swarm Optimization

2013-03-04 Computers
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Release : 2013-03-04
Genre : Computers
Kind : eBook
Book Rating : 97X/5 ( reviews)

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Book Synopsis Particle Swarm Optimization by : Maurice Clerc

Download or read book Particle Swarm Optimization written by Maurice Clerc. This book was released on 2013-03-04. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.

Applying Particle Swarm Optimization

2021-05-13 Business & Economics
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Release : 2021-05-13
Genre : Business & Economics
Kind : eBook
Book Rating : 812/5 ( reviews)

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Book Synopsis Applying Particle Swarm Optimization by : Burcu Adıgüzel Mercangöz

Download or read book Applying Particle Swarm Optimization written by Burcu Adıgüzel Mercangöz. This book was released on 2021-05-13. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz’s portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio’s decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset. The book explains PSO in detail and demonstrates how to implement Markowitz’s portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.

Particle Swarm Optimisation

2016-04-19 Computers
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Release : 2016-04-19
Genre : Computers
Kind : eBook
Book Rating : 772/5 ( reviews)

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Book Synopsis Particle Swarm Optimisation by : Jun Sun

Download or read book Particle Swarm Optimisation written by Jun Sun. This book was released on 2016-04-19. Available in PDF, EPUB and Kindle. Book excerpt: Although the particle swarm optimisation (PSO) algorithm requires relatively few parameters and is computationally simple and easy to implement, it is not a globally convergent algorithm. In Particle Swarm Optimisation: Classical and Quantum Perspectives, the authors introduce their concept of quantum-behaved particles inspired by quantum mechanics, which leads to the quantum-behaved particle swarm optimisation (QPSO) algorithm. This globally convergent algorithm has fewer parameters, a faster convergence rate, and stronger searchability for complex problems. The book presents the concepts of optimisation problems as well as random search methods for optimisation before discussing the principles of the PSO algorithm. Examples illustrate how the PSO algorithm solves optimisation problems. The authors also analyse the reasons behind the shortcomings of the PSO algorithm. Moving on to the QPSO algorithm, the authors give a thorough overview of the literature on QPSO, describe the fundamental model for the QPSO algorithm, and explore applications of the algorithm to solve typical optimisation problems. They also discuss some advanced theoretical topics, including the behaviour of individual particles, global convergence, computational complexity, convergence rate, and parameter selection. The text closes with coverage of several real-world applications, including inverse problems, optimal design of digital filters, economic dispatch problems, biological multiple sequence alignment, and image processing. MATLAB®, Fortran, and C++ source codes for the main algorithms are provided on an accompanying downloadable resources. Helping you numerically solve optimisation problems, this book focuses on the fundamental principles and applications of PSO and QPSO algorithms. It not only explains how to use the algorithms, but also covers advanced topics that establish the groundwork for understanding.

Optimization for Machine Learning

2021-09-22 Computers
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Release : 2021-09-22
Genre : Computers
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Optimization for Machine Learning by : Jason Brownlee

Download or read book Optimization for Machine Learning written by Jason Brownlee. This book was released on 2021-09-22. Available in PDF, EPUB and Kindle. Book excerpt: Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.

Particle Swarm Optimization (PSO)

2017 Electric power systems
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Release : 2017
Genre : Electric power systems
Kind : eBook
Book Rating : 286/5 ( reviews)

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Book Synopsis Particle Swarm Optimization (PSO) by : Brian Walker

Download or read book Particle Swarm Optimization (PSO) written by Brian Walker. This book was released on 2017. Available in PDF, EPUB and Kindle. Book excerpt: Particle swarm optimisation (PSO) is one of the recently developed swarm intelligent optimisation technologies that offer the advantages of simplicity and fast biological convergence. The technique originated from the theory of artificial life and evolution, which is based on the optimisation that is achieved as a result of swarm behaviour. PSO can be easily implemented due to fewer parameters for adjustment hence it has been applied broadly in various engineering fields. This book reviews advances in research and applications of PSO.

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