Swarm Intelligence

Table of Contents

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   Book Overview
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   Chapter 1 A Study on Force-Based Collaboration in Flying Swarms
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   Chapter 2 Automatic Design of Communication-Based Behaviors for Robot Swarms
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   Chapter 3 Behavior Trees as a Control Architecture in the Automatic Modular Design of Robot Swarms
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   Chapter 4 Guidance of Swarms with Agents Having Bearing Only and Limited Visibility Sensors
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   Chapter 5 Hybrid Control of Swarms for Resource Selection
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   Chapter 6 Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
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   Chapter 7 Morphogenesis as a Collective Decision of Agents Competing for Limited Resource: A Plants Approach
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   Chapter 8 Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics
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    Chapter 9 On Mimicking the Effects of the Reality Gap with Simulation-Only Experiments
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    Chapter 10 Optimization of Swarm Behavior Assisted by an Automatic Local Proof for a Pattern Formation Task
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    Chapter 11 Quality-Sensitive Foraging by a Robot Swarm Through Virtual Pheromone Trails
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    Chapter 12 Search in a Maze-Like Environment with Ant Algorithms: Complexity, Size and Energy Study
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    Chapter 13 Self-adaptive Quantum Particle Swarm Optimization for Dynamic Environments
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    Chapter 14 Simulating Kilobots Within ARGoS: Models and Experimental Validation
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    Chapter 15 Simulating Multi-robot Construction in ARGoS
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    Chapter 16 Stability Analysis of the Multi-objective Multi-guided Particle Swarm Optimizer
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    Chapter 17 Swarm Attack: A Self-organized Model to Recover from Malicious Communication Manipulation in a Swarm of Simple Simulated Agents
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    Chapter 18 Task-Agnostic Evolution of Diverse Repertoires of Swarm Behaviours
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    Chapter 19 The Best-of-n Problem with Dynamic Site Qualities: Achieving Adaptability with Stubborn Individuals
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    Chapter 20 The Impact of Interaction Models on the Coherence of Collective Decision-Making: A Case Study with Simulated Locusts
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    Chapter 21 The Importance of Component-Wise Stochasticity in Particle Swarm Optimization
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    Chapter 22 The Importance of Information Flow Regulation in Preferentially Foraging Robot Swarms
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    Chapter 23 The Role of Largest Connected Components in Collective Motion
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    Chapter 24 Why the Intelligent Water Drops Cannot Be Considered as a Novel Algorithm
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    Chapter 25 A Cooperative Opposite-Inspired Learning Strategy for Ant-Based Algorithms
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    Chapter 26 A Solution for the Team Selection Problem Using ACO
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    Chapter 27 Boundary Constraint Handling Techniques for Particle Swarm Optimization in High Dimensional Problem Spaces
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    Chapter 28 Does the ACO$$\mathbb {_R}$$ Algorithm Benefit from the Use of Crossover?
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    Chapter 29 Embodied Evolution of Self-organised Aggregation by Cultural Propagation
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    Chapter 30 Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems
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    Chapter 31 Gaussian-Valued Particle Swarm Optimization
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    Chapter 32 Individual Activity Level and Mobility Patterns of Ants Within Nest Site
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    Chapter 33 Learning Based Leadership in Swarm Navigation
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    Chapter 34 Maintaining Diversity in Robot Swarms with Distributed Embodied Evolution
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    Chapter 35 On Steering Swarms
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    Chapter 36 Vector Field Benchmark for Collective Search in Unknown Dynamic Environments