making it Fiveable...
1.1
Definition and principles of swarm intelligence
1.2
Historical development of swarm intelligence
1.3
Key characteristics of swarm systems
1.4
Swarm intelligence in nature
1.5
Artificial swarm intelligence
2.1
Ant colonies
2.2
Bee swarms
2.3
Bird flocking
2.4
Fish schooling
2.5
Bacterial colonies
3.1
Particle swarm optimization
3.2
Ant colony optimization
3.3
Artificial bee colony algorithm
3.4
Firefly algorithm
3.5
Bacterial foraging optimization
4.1
Centralized vs. decentralized control
4.2
Homogeneous vs. heterogeneous swarms
4.3
Scalability in swarm systems
4.4
Robustness and fault tolerance
4.5
Swarm topologies
5.1
Consensus algorithms
5.2
Quorum sensing
5.3
Collective perception
5.4
Distributed problem-solving
5.5
Swarm cognition
6.1
Local interactions
6.2
Stigmergy
6.3
Pheromone-based communication
6.4
Flocking and formation control
6.5
Information sharing in swarms
7.1
Self-organized task allocation
7.2
Market-based approaches
7.3
Threshold-based models
7.4
Learning and adaptation in task allocation
7.5
Multi-task swarms
8.1
Principles of self-organization
8.2
Emergence in complex systems
8.3
Pattern formation
8.4
Collective transport
8.5
Swarm aggregation and dispersion
9.1
Search and rescue
9.2
Environmental monitoring
9.3
Exploration and mapping
9.4
Agriculture and precision farming
9.5
Swarm-based manufacturing
10.1
Distributed sensing
10.2
Sensor fusion
10.3
10.4
Environmental mapping
10.5
Obstacle detection and avoidance
11.1
Miniaturization and micro-robotics
11.2
Modular robotics
11.3
Soft robotics in swarms
11.4
Energy efficiency and power management
11.5
Swarm simulation platforms
12.1
Safety and security in swarm systems
12.2
Privacy concerns
12.3
Ethical decision-making in autonomous swarms
12.4
Human-swarm interaction
12.5
Future directions in swarm robotics research