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Ant Colony Optimization and Swarm Intelligence

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Cover of 'Ant Colony Optimization and Swarm Intelligence'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Comparison Between ACO Algorithms for the Set Covering Problem
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    Chapter 2 A VLSI Multiplication-and-Add Scheme Based on Swarm Intelligence Approaches
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    Chapter 3 ACO for Continuous and Mixed-Variable Optimization
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    Chapter 4 An Ant Approach to Membership Overlay Design
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    Chapter 5 An Ant Colony Optimisation Algorithm for the Set Packing Problem
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    Chapter 6 An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP
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    Chapter 7 An External Memory Implementation in Ant Colony Optimization
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    Chapter 8 BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior
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    Chapter 9 Competition Controlled Pheromone Update for Ant Colony Optimization
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    Chapter 10 Cooperative Transport of Objects of Different Shapes and Sizes
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    Chapter 11 Deception in Ant Colony Optimization
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    Chapter 12 Evolution of Direct Communication for a Swarm-bot Performing Hole Avoidance
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    Chapter 13 Gathering Multiple Robotic A(ge)nts with Limited Sensing Capabilities
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    Chapter 14 Improvements on Ant Routing for Sensor Networks
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    Chapter 15 Integrating ACO and Constraint Propagation
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    Chapter 16 Logistic Constraints on 3D Termite Construction
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    Chapter 17 Modeling Ant Behavior Under a Variable Environment
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    Chapter 18 Multi-type Ant Colony: The Edge Disjoint Paths Problem
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    Chapter 19 On the Design of ACO for the Biobjective Quadratic Assignment Problem
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    Chapter 20 Reasons of ACO’s Success in TSP
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    Chapter 21 S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty
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    Chapter 22 Time-Scattered Heuristic for the Hardware Implementation of Population-Based ACO
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    Chapter 23 Ad Hoc Networking with Swarm Intelligence
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    Chapter 24 An Ant Colony Heuristic for the Design of Two-Edge Connected Flow Networks
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    Chapter 25 An Experimental Analysis of Loop-Free Algorithms for Scale-Free Networks
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    Chapter 26 An Experimental Study of the Ant Colony System for the Period Vehicle Routing Problem
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    Chapter 27 An Extension of Ant Colony System to Continuous Optimization Problems
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    Chapter 28 Ant Algorithms for Urban Waste Collection Routing
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    Chapter 29 Ants Can Play Music
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    Chapter 30 Backtracking Ant System for the Traveling Salesman Problem
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    Chapter 31 Colored Ants for Distributed Simulations
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    Chapter 32 Dynamic Routing in Mobile Wireless Networks Using ABC-AdHoc
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    Chapter 33 Fuzzy Ant Based Clustering
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    Chapter 34 How to Use Ants for Hierarchical Clustering
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    Chapter 35 Inversing Mechanical Parameters of Concrete Gravity Dams Using Ant Colony Optimization
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    Chapter 36 Large Pheromones: A Case Study with Multi-agent Physical A*
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    Chapter 37 Near Parameter Free Ant Colony Optimisation
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    Chapter 38 Particle Swarm Optimization Algorithm for Permutation Flowshop Sequencing Problem
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    Chapter 39 Search Bias in Constructive Metaheuristics and Implications for Ant Colony Optimisation
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    Chapter 40 Task Oriented Functional Self-organization of Mobile Agents Team: Memory Optimization Based on Correlation Feature
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    Chapter 41 Towards a Real Micro Robotic Swarm
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    Chapter 42 A Hybrid Ant Colony System Approach for the Capacitated Vehicle Routing Problem
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    Chapter 43 A Swarm-Based Approach for Selection of Signal Plans in Urban Scenarios
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    Chapter 44 Ant Colony Behaviour as Routing Mechanism to Provide Quality of Service
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    Chapter 45 Applying Ant Colony Optimization to the Capacitated Arc Routing Problem
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    Chapter 46 Dynamic Optimization Through Continuous Interacting Ant Colony
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    Chapter 47 Dynamic Routing in Traffic Networks Using AntNet
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    Chapter 48 First Competitive Ant Colony Scheme for the CARP
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    Chapter 49 Hypothesis Corroboration in Semantic Spaces with Swarming Agents
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    Chapter 50 Mesh-Partitioning with the Multiple Ant-Colony Algorithm
Overall attention for this book and its chapters
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Title
Ant Colony Optimization and Swarm Intelligence
Published by
Lecture notes in computer science, January 2004
DOI 10.1007/b99492
ISBNs
978-3-54-022672-7, 978-3-54-028646-2
Authors

Marco Dorigo, Mauro Birattari, Christian Blum, Luca Maria Gambardella, Francesco Mondada, Thomas Stützle

Editors

Dorigo, Marco, Birattari, Mauro, Blum, Christian, Gambardella, Luca Maria, Mondada, Francesco, Stützle, Thomas

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 65 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 2%
Student > Ph. D. Student 1 2%
Researcher 1 2%
Unknown 62 95%
Readers by discipline Count As %
Engineering 2 3%
Computer Science 1 2%
Unknown 62 95%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 September 2023.
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#16,528,289
of 24,317,326 outputs
Outputs from Lecture notes in computer science
#4,702
of 8,151 outputs
Outputs of similar age
#118,335
of 138,747 outputs
Outputs of similar age from Lecture notes in computer science
#83
of 115 outputs
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