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Swarm Intelligence

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Cover of 'Swarm Intelligence'

Table of Contents

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    Book Overview
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    Chapter 1 A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network
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    Chapter 2 A New Approach for Making Use of Negative Learning in Ant Colony Optimization
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    Chapter 3 Ant Colony Optimization for Object-Oriented Unit Test Generation
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    Chapter 4 Branched Structure Formation in a Decentralized Flock of Wheeled Robots
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    Chapter 5 Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing
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    Chapter 6 Constrained Scheduling of Step-Controlled Buffering Energy Resources with Ant Colony Optimization
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    Chapter 7 Construction Task Allocation Through the Collective Perception of a Dynamic Environment
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    Chapter 8 Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm
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    Chapter 9 Dynamic Response Thresholds: Heterogeneous Ranges Allow Specialization While Mitigating Convergence to Sink States
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    Chapter 10 Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty
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    Chapter 11 Guerrilla Performance Analysis for Robot Swarms: Degrees of Collaboration and Chains of Interference Events
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    Chapter 12 Heterogeneous Response Intensity Ranges and Response Probability Improve Goal Achievement in Multi-agent Systems
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    Chapter 13 HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence
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    Chapter 14 Memory Induced Aggregation in Collective Foraging
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    Chapter 15 Modeling Pathfinding for Swarm Robotics
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    Chapter 16 Motion Dynamics of Foragers in Honey Bee Colonies
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    Chapter 17 Multi-robot Coverage Using Self-organized Networks for Central Coordination
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    Chapter 18 Robot Distancing: Planar Construction with Lanes
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    Chapter 19 The Pi-puck Ecosystem: Hardware and Software Support for the e-puck and e-puck2
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    Chapter 20 Zealots Attack and the Revenge of the Commons: Quality vs Quantity in the Best-of-n
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    Chapter 21 AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines
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    Chapter 22 Coalition Formation Problem: A Group Dynamics Inspired Swarming Method
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    Chapter 23 Collective Gradient Perception in a Flocking Robot Swarm
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    Chapter 24 Fitting Gaussian Mixture Models Using Cooperative Particle Swarm Optimization
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    Chapter 25 Formation Control of UAVs and Mobile Robots Using Self-organized Communication Topologies
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    Chapter 26 Group-Size Regulation in Self-organized Aggregation in Robot Swarms
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    Chapter 27 On the Effects of Minimally Invasive Collision Avoidance on an Emergent Behavior
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    Chapter 28 Set-Based Particle Swarm Optimization for Portfolio Optimization
Attention for Chapter 28: Set-Based Particle Swarm Optimization for Portfolio Optimization
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Chapter title
Set-Based Particle Swarm Optimization for Portfolio Optimization
Chapter number 28
Book title
Swarm Intelligence
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-60376-2_28
Book ISBNs
978-3-03-060375-5, 978-3-03-060376-2

Kyle Erwin, Andries P. Engelbrecht

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 25%
Other 1 25%
Unknown 2 50%
Readers by discipline Count As %
Unspecified 1 25%
Business, Management and Accounting 1 25%
Unknown 2 50%