swarm intelligence optimization algorithms (35 risultati)

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Paperback. Condizione: Brand New. 72 pages. 8.66x5.91x0.17 inches. In Stock.

Taschenbuch. Condizione: Neu. Automatic Speed Tracking of PMSM Drive: A Swarm Intelligence Approach | Optimal PID Gain Scheduling for Speed Control of PMSM Drive using Bio Inspired Optimization Algorithms | Satish Kumar Injeti (u. a.) | Taschenbuch | 72 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330023680 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Condizione: New. Softcover reprint of the original 1st ed. 2017 edition NO-PA16APR2015-KAP.

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Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Taschenbuch. Condizione: Neu. Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications | Modestus O. Okwu (u. a.) | Taschenbuch | xii | Englisch | 2021 | Springer | EAN 9783030611132 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examplesincluded in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examplesincluded in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

Hardcover. Condizione: Brand New. 204 pages. 9.25x6.10x0.71 inches. In Stock.

Paperback. Condizione: Brand New. 204 pages. 9.25x6.10x0.46 inches. In Stock.

Hardcover. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condizione: Brand New. 362 pages. 9.00x6.00x1.00 inches. In Stock.

Condizione: 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:.

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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - PID controller is a most popular controller in industry. The performance of PID controller depends on proper scheduling of gains which is a difficult task due to uncertainties in the industry. In this context this book presents an efficient methodology for automatic speed tracking of PMSM drive using bio inspired optimization based PID controller. PSO, BAT and FFA based online gain scheduling of PID controller for speed control of PMSM drive was developed and implemented with the help of various objective functions. From the obtained results it can concluded that among ISE, IAE, ITSE and ITAE, ITAE is the best objective function for gain scheduling of PID controller for speed control of PMSM drive. Among PSO+PID, BAT+PID and FFA+PID, PSO+PID are proven to be robust as it was applied to control the speed of PMSM drive at different reference speeds. I hope this book is most useful for the researchers and masters level students who were in the area of control of electric drives.

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Condizione: new. Questo è un articolo print on demand.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper op.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examplesincluded in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences. 204 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examplesincluded in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences. 204 pp. Englisch.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduction to metaheuristic techniques and algorithms, biomimicry and nature-inspired algorithms with swarm intelligence and presents the basics of the algorithmsProvides a guide of how to develop algorithms from nature-insp.

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