swarm intelligence optimization algorithms (8 Ergebnisse)

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Taschenbuch. Zustand: 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.

Buch. Zustand: Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch.

Taschenbuch. Zustand: 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. Zustand: 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. Zustand: Brand New. 204 pages. 9.25x6.10x0.71 inches. In Stock.

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

Zustand: 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:.