Swarm Intelligence Optimization Algorithm (41 results)

paperback. Condition: New. Paperback. Pub Date :2012-08-01 Publisher: Science Press only genuine brand new book No picture can look at Baidu the check book title ISBN pricing Publishing House information; only puerile - you can not counter-offer can not package Post. free open textbook amount invoice no taxes. books dedicated machine-printed invoices.…

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Taschenbuch. Condition: Neu. Numerical Optimization & Swarm Intelligence for optimization | Trust Region Algorithm & Particle Swarm Optimization | Mohammed A. El-Shorbagy (u. a.) | Taschenbuch | 148 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659483950 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

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Condition: New. pp. 362 Softcover reprint of the original 1st ed. 2015 edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modeling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metahuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.…

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modeling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metahuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.…

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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 364 | Sprache: Englisch | Produktart: Bücher | This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modeling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metahuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.…

Paperback. Condition: Brand New. reprint edition. 323 pages. 9.25x6.10x0.82 inches. In Stock.

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Hardcover. Condition: gut. 2014. Emerging Research on Swarm Intelligence and Algorithm Optimization In englischer Sprache. pages.

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Source: Wikipedia. Pages: 90. Chapters: Newton's method, Genetic algorithm, Greedy algorithm, Dynamic programming, Minimax, Alpha-beta pruning, Random optimization, Simulated annealing, CMA-ES, Simplex algorithm, Swarm intelligence, Particle swarm optimization, Criss-cross algorithm, Imperialist competitive algorithm, Divide and conquer algorithm, Harmony search, Bees algorithm, Differential evolution, Matrix chain multiplication, Bin packing problem, Evolutionary algorithm, Nelder¿Mead method, Extremal optimization, Hill climbing, IOSO, Reactive search optimization, Cutting-plane method, Guided Local Search, Automatic label placement, Karmarkar's algorithm, Cuckoo search, Evolutionary multi-modal optimization, Job shop scheduling, Cross-entropy method, Meta-optimization, Interior point method, Crew scheduling, Auction algorithm, Artificial Bee Colony Algorithm, Tabu search, Augmented Lagrangian method, Firefly algorithm, BRST algorithm, Quantum annealing, Pattern search, Graduated optimization, Branch and bound, Fourier¿Motzkin elimination, Random search, Bland's rule, Maximum subarray problem, Negamax, Genetic algorithms in economics, Tree rearrangement, Glowworm swarm optimization, Sequential minimal optimization, Branch and cut, Delayed column generation, Very large-scale neighborhood search, Mehrotra predictor-corrector method, Penalty method, BHHH algorithm, Evolutionary programming, Destination dispatch, Great Deluge algorithm, Iterated local search, Big M method, Lemke's algorithm, Sequence-dependent setup, Ordered subset expectation maximization, MCS algorithm, Zionts¿Wallenius method, Biologically inspired algorithms, Rosenbrock methods, Stochastic hill climbing, Optimization algorithm. Excerpt: A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In a genetic algorithm, a population of strings (called chromosomes or the genotype of the genome), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), and modified (recombined and possibly randomly mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. If the algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have been reached. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics and other fields. A typical genetic algorithm requires..…

paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2020-05-01 Pages: 236 Publisher: China Economic Press Optimization algorithm swarm intelligence intelligent optimization has become a hot topic. and permeated every aspect of production and life. As a new intelligent optimization technology. swarm intelligence optimization algorithm proposed since. widely used in artificial intelligence. communication networks. and industrial manufacturing markets. no.…

paperback. Condition: New. Paperback. Pub Date: 2021-06-01 Pages: 201 Language: Chinese Publisher: Science Press uses time series forecasting technology to perform statistical analysis on data. and can speculate on the future trend of the development of things.?However. the traditional time series forecasting technology model is simple to construct. and the information mining and analysis contained in the data is not deep enough.?Therefore. the use of forecasting theory based on swarm intelligence optimization algorith.…

paperback. Condition: New. Paperback. Pub Date: 2017-06-01 Pages: $number Language: Chinese publisher: Scientific Publishing House This book is aimed at the frontier field of intelligent information processing. aiming at the key problems of swarm intelligence optimization algorithm and its application. This paper discusses the new Swarm intelligence optimization algorithm and the Swarm Intelligence optimization algorithm in the three-dimensional model processing and the trustworthy software test .…

paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2018-07-01 Pages: 372 Publisher: Tsinghua University Press This book focuses on the optimization of initial weight vector in blind equilibrium theory and algorithm research. using intelligent group algorithm and intelligent computing theory as tools to develop blindness. Research on performance optimization of equalization algorithms. The main contents are: Based on genetic algorithm (including adaptive genetic algorithm. simulated annealing genetic algor.…

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