Search Results: 'Ant Colony Optimization'
A Data Clustering Approach: Using Ant Colony Optimization
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Ant colony optimization has been successfully applied to many combinatorial optimization problem. It has been firstly implemented for traveling salesman problem which is well known problem in... More > engineering science. One chemical substance called pheromone plays very important role in any ant colony optimization algorithm. Apart from foraging behavior another behavior called brood shorting is also notify in some species of real ants.Ants will classify to that cluster which is having more similar to that ant and like that the whole tree is built. The branches to the root is considered to be independent clusters discovered by this algorithm and by this way the number of clusters identified by this approach is the number of braches made to the root node of the AntTree. Here depending on order of we feed the ant as input to the algorithm sometimes it might be possible to wrongly cluster the data. Thus it will increase the probability of clustering algorithm.< Less
Clever Algorithms: Nature-Inspired Programming Recipes
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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
Essentials of Metaheuristics (Second Edition)
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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
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