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Mauro Birattari; Christian Blum; Marco Dorigo; Luca M. Gambardella; Francesco Mondada; Thomas Stutzle:

Ant Colony Optimization and Swarm Intelligence - neues Buch

2004, ISBN: 9783540286462

4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004, Proceeding, eBooks, eBook Download (PDF), [PU: Springer Berlin Heidelberg], Springer Berlin Heidelberg, 2004

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Ant Colony Optimization and Swarm Intelligence : 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004, Proceeding - neues Buch

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Ant Colony Optimization and Swarm Intelligence - Marco Dorigo; Mauro Birattari; Christian Blum; Luca M. Gambardella; Francesco Mondada; Thomas Stützle
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Marco Dorigo; Mauro Birattari; Christian Blum; Luca M. Gambardella; Francesco Mondada; Thomas Stützle:
Ant Colony Optimization and Swarm Intelligence - neues Buch

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Ant Colony Optimization and Swarm Intelligence - Marco Dorigo; Mauro Birattari; Christian Blum; Luca M. Gambardella; Francesco Mondada; Thomas Stützle
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Marco Dorigo; Mauro Birattari; Christian Blum; Luca M. Gambardella; Francesco Mondada; Thomas Stützle:
Ant Colony Optimization and Swarm Intelligence - neues Buch

ISBN: 9783540286462

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Details zum Buch

Detailangaben zum Buch - Ant Colony Optimization and Swarm Intelligence


EAN (ISBN-13): 9783540286462
Erscheinungsjahr: 2004
Herausgeber: Springer Berlin Heidelberg

Buch in der Datenbank seit 2017-01-14T19:04:35+01:00 (Berlin)
Detailseite zuletzt geändert am 2022-05-21T22:38:58+02:00 (Berlin)
ISBN/EAN: 9783540286462

ISBN - alternative Schreibweisen:
978-3-540-28646-2
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: mauro, marco luca, dorigo, thomas blum
Titel des Buches: swarm intelligence, ant colony optimization, brussels 2004


Daten vom Verlag:

Autor/in: Marco Dorigo; Mauro Birattari; Christian Blum; Luca M. Gambardella; Francesco Mondada; Thomas Stützle
Titel: Lecture Notes in Computer Science; Ant Colony Optimization and Swarm Intelligence - 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004, Proceeding
Verlag: Springer; Springer Berlin
438 Seiten
Erscheinungsjahr: 2004-11-24
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
XIV, 438 p.

EA; E107; eBook; Nonbooks, PBS / Mathematik/Sonstiges; Optimierung; Verstehen; Ant algorithm; algorithms; ant algorithms; ant colonies; ant colony optimization; ant programming; autonomous agent; distributed control; intelligence; multi-agent systems; networking; operations research; optimization; roboties; swarm intelligence; algorithm analysis and problem complexity; C; Optimization; Computer Science; Algorithms; Theory of Computation; Numerical Analysis; Discrete Mathematics in Computer Science; Computer Science; Informatik; Algorithmen und Datenstrukturen; Theoretische Informatik; Numerische Mathematik; Mathematik für Informatiker; Diskrete Mathematik; BC

A Comparison Between ACO Algorithms for the Set Covering Problem.- A Comparison Between ACO Algorithms for the Set Covering Problem.- A VLSI Multiplication-and-Add Scheme Based on Swarm Intelligence Approaches.- ACO for Continuous and Mixed-Variable Optimization.- An Ant Approach to Membership Overlay Design.- An Ant Colony Optimisation Algorithm for the Set Packing Problem.- An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP.- An External Memory Implementation in Ant Colony Optimization.- BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior.- Competition Controlled Pheromone Update for Ant Colony Optimization.- Cooperative Transport of Objects of Different Shapes and Sizes.- Deception in Ant Colony Optimization.- Evolution of Direct Communication for a Swarm-bot Performing Hole Avoidance.- Gathering Multiple Robotic A(ge)nts with Limited Sensing Capabilities.- Improvements on Ant Routing for Sensor Networks.- Integrating ACO and Constraint Propagation.- Logistic Constraints on 3D Termite Construction.- Modeling Ant Behavior Under a Variable Environment.- Multi-type Ant Colony: The Edge Disjoint Paths Problem.- On the Design of ACO for the Biobjective Quadratic Assignment Problem.- Reasons of ACO’s Success in TSP.- S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty.- Time-Scattered Heuristic for the Hardware Implementation of Population-Based ACO.- Short Papers.- Ad Hoc Networking with Swarm Intelligence.- An Ant Colony Heuristic for the Design of Two-Edge Connected Flow Networks.- An Experimental Analysis of Loop-Free Algorithms for Scale-Free Networks.- An Experimental Study of the Ant Colony System for the Period Vehicle Routing Problem.- An Extension ofAnt Colony System to Continuous Optimization Problems.- Ant Algorithms for Urban Waste Collection Routing.- Ants Can Play Music.- Backtracking Ant System for the Traveling Salesman Problem.- Colored Ants for Distributed Simulations.- Dynamic Routing in Mobile Wireless Networks Using ABC-AdHoc.- Fuzzy Ant Based Clustering.- How to Use Ants for Hierarchical Clustering.- Inversing Mechanical Parameters of Concrete Gravity Dams Using Ant Colony Optimization.- Large Pheromones: A Case Study with Multi-agent Physical A*.- Near Parameter Free Ant Colony Optimisation.- Particle Swarm Optimization Algorithm for Permutation Flowshop Sequencing Problem.- Search Bias in Constructive Metaheuristics and Implications for Ant Colony Optimisation.- Task Oriented Functional Self-organization of Mobile Agents Team: Memory Optimization Based on Correlation Feature.- Towards a Real Micro Robotic Swarm.- Posters.- A Hybrid Ant Colony System Approach for the Capacitated Vehicle Routing Problem.- A Swarm-Based Approach for Selection of Signal Plans in Urban Scenarios.- Ant Colony Behaviour as Routing Mechanism to Provide Quality of Service.- Applying Ant Colony Optimization to the Capacitated Arc Routing Problem.- Dynamic Optimization Through Continuous Interacting Ant Colony.- Dynamic Routing in Traffic Networks Using AntNet.- First Competitive Ant Colony Scheme for the CARP.- Hypothesis Corroboration in Semantic Spaces with Swarming Agents.- Mesh-Partitioning with the Multiple Ant-Colony Algorithm.
Includes supplementary material: sn.pub/extras

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