Search Results: 'Particle Swarm Optimization'


6 results for "Particle Swarm Optimization"
System Of Intrusion Detection Using Particle Swarm Optimization By Hamza Benjabbour
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Rules based system for generating intrusion detection rule by using Particle Swarm Optimisation.
A Metaheuristic Approach to Parallel Inversion of Large Random Matrices :: Precoding/Decoding for Large Scale MIMO Systems Using Particle Swarm Optimization By Muhammad Ali Raza Anjum
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A novel method is presented for the inversion of large random channel matrices that arise in large scale MIMO systems. The proposed method eliminates the need for forming the pseudoinverse... More > altogether. It is based on the idea of inverse vectors in which each column in the channel matrix is viewed as a particle in a swarm that seeks its own solution, the inverse vector. Such an inverse vector has to satisfy two constraints. Firstly, it has to be in the null-space of all the remaining columns. Secondly, it has to form a projection of value equal to one in the direction of selected column. These constraints open up an avenue for metaheuristic methods to enter the realm of large random matrix inversion. A case for Particle Swarm Optimization has been presented which when employed to demonstrate the new approach yields much better performance in terms of residue and computation time. But the opportunity is still there for rest of the methods. Enter the realm of large scale MIMO systems by trying one of your own!< Less
Clever Algorithms: Nature-Inspired Programming Recipes By Jason Brownlee
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This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete,... More > consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.< Less
Optimización de Enjambre de Partículas aplicada al Problema del Cajero Viajante bi-objetivo By Joaquín Quinto Lima Molinari
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Este trabajo describe la aplicación de métodos basados en la Metaheurística de Optimización de Enjambre de Partículas para la resolución del Problema del... More > Cajero Viajante en un contexto de optimización multiobjetivo.< Less
Essentials of Metaheuristics (Second Edition) By Sean Luke
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Interested in the Genetic Algorithm? Simulated Annealing? Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms, and is intended for undergraduate... More > students, programmers, and non-experts. The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 71 figures and 135 algorithms great and small. Algorithms include: Gradient Ascent techniques, Hill-Climbing variants, Simulated Annealing, Tabu Search variants, Iterated Local Search, Evolution Strategies, the Genetic Algorithm, the Steady-State Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Genetic Programming variants, One- and Two-Population Competitive Coevolution, N-Population Cooperative Coevolution, Implicit Fitness Sharing, Deterministic Crowding, NSGA-II, SPEA2, GRASP, Ant Colony Optimization variants, Guided Local Search, LEM, PBIL, UMDA, cGA, BOA, SAMUEL, ZCS, XCS, and XCSF.< Less
MOEA Framework 2.0 User Manual By David Hadka
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The MOEA Framework is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose multiobjective optimization... More > algorithms. The MOEA Framework supports genetic algorithms, differential evolution, particle swarm optimization, genetic programming, grammatical evolution, and more. A number of algorithms are provided out-of-the-box, including NSGA-II, ε-MOEA, GDE3 and MOEA/D. In addition, the MOEA Framework provides the tools necessary to rapidly design, develop, execute and statistically test optimization algorithms. This user manual is available for free at as a PDF document. 100% of the proceeds from book sales support the continued development and maintenance of the MOEA Framework.< Less

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Left to Die Left to Die By Wes Rand
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