The document discusses swarm intelligence, a study of decentralized, self-organized systems that solve complex problems inspired by natural systems like flocks of birds and ant colonies. It highlights key principles, various swarm algorithms such as ant colony optimization and particle swarm optimization, and their applications in swarm robotics. The future of swarm intelligence includes advancements in IoT, wildlife conservation, and ethical considerations in technology usage.

1. S W A R M
C O M P U T I N G
BY GROUP 13:
PATEL SMITKUMAR
V.
VAGHELA DHRUV K.
PATEL RIYA H.
Panchal MANAV P,
SHAH SAGAR U.

2. TA B L E O F C O N T E N T S
What is swarm
intelligence ?
Why Swarm
Intelligence
Matters
Key Principles of
Swarm
Intelligence
Natural
Examples of
Swarm
Intelligence
Algorithms
Inspired by
Swarm
Intelligence
Ant Colony
Optimization
(ACO)
Particle Swarm
Optimization
(PSO)
Swarm Robotics
Applications
Advantages of
Swarm
Intelligence
Challenges of
Swarm
Intelligence
Future of
Swarm
Intelligence
Q&A

3. S E L F D R I V I N G C A R
What are the
technology or concepts
used in Self driving
car ?

4. E D G E C O M P U T I N G
• Edge computing is a distributed
computing framework that
processes data closer to the
source, rather than sending it to a
central data center. This technology
enables devices to process data
quickly and act on it in real time.

5. W H AT I S S W A R M I N T E L L I G E N C E ?
• Swarm intelligence is the study of how decentralized, self-organized systems
behave collectively, and how to simulate that behavior to solve complex
problems. The term was coined in 1989 by Gerardo Beni and Jing Wang in the
context of cellular robotic systems.
• Swarm intelligence is inspired by natural systems, such as flocks of birds, schools
of fish, and ant colonies, where groups act and react without a single leader. The
agents in a swarm exchange information with each other and the environment,
following basic rules that lead to global system behavior.

6. W H Y S W A R M I N T E L L I G E N C E M AT T E R S
• Understanding swarm intelligence allows
us to design distributed systems that
mirror nature’s efficiency and adaptability.
• Swarm intelligence follows the concept ,
where a single task is distributed amongst
multiple entities within a group. Since the
task is now performed by multiple group
members, the process is more efficient,
and the results are more accurate

7. K E Y P R I N C I P L E S O F S W A R M
I N T E L L I G E N C E
• Decentralization
• Self-Organization
• Local Interactions
• Collective Intelligence
• Adaptivity
• Exploration and Exploitation
• Scalability
• Robustness

8. N AT U R A L E X A M P L E S O F S W A R M
I N T E L L I G E N C E
Flock of Birds Schools of fish Ant colonies

9. A L G O R I T H M S I N S P I R E D B Y S W A R M
I N T E L L I G E N C E
• Ant Colony Optimization (ACO)
• Particle Swarm Optimization (PSO):
• Artificial Bee Colony (ABC) Algorithm
• Glowworm Swarm Optimization (GSO)
• Cuckoo Search Algorithm
• Firefly Algorithm
• Bat Algorithm
• Fish Schooling Optimization (FSO)
• Termite Colony Optimization (TCO)
• Ant Lion Optimizer (ALO)

10. A N T C O L O N Y O P T I M I Z AT I O N ( A C O )
• Inspiration: This algorithm is inspired by the foraging behavior of ants. Ants
deposit pheromones as they move, which influences other ants to follow the
path. Over time, shorter paths accumulate more pheromones, guiding other ants
to find the most efficient route.
• Application: ACO is primarily used for optimization problems, such as the
traveling salesman problem (TSP), vehicle routing, and network routing.

11. P A R T I C L E S W A R M
O P T I M I Z AT I O N ( P S O )
• is inspired by the flocking behavior of birds and schooling of fish. Particles
(agents) move through the solution space, adjusting their positions based on
their previous experiences and the experiences of their neighbors
• Application: PSO is widely used in optimization tasks like function optimization,
machine learning model tuning, and control system design.
• Key Idea: Each particle has a velocity and a position. It adjusts its position based
on its own best position and the best position found by its neighbors, iterating
until convergence to an optimal solution.

12. S W A R M R O B O T I C S A P P L I C AT I O N S
https://youtube.com/shorts/usYWrB6w5TM?
si=Hqsu56iyWU88Jaok
https://youtu.be/oD9Psya3uHY?si=3S0jVJNJ6tPhzBk4 08:05 -

13. S W A R M R O B O T I C S A D V A N TA G E S
• Scalability- Handles Large-Scale Problems, Flexible with Increased Agents
• Adaptability- Responsive to Environmental Changes, No Central Control
• Robustness and Fault Tolerance- Resilient to Agent Failure, Redundancy of Agents
• Efficiency and Optimization- Cost-Effective Solutions, Collective Problem Solving
• Distributed Processing and Parallelism- Parallel Computation, No Bottlenecks
• Simplicity of Individual Agents- Low Computational Cost, Easy to Implement
• Exploration and Exploitation Balance- Effective Search Mechanisms, Avoids Premature
Convergence
• Real-World Applications Across Domains- Versatile Applications, Adaptable to Complex Systems

14. S W A R M R O B O T I C S C H A L L E N G E S
• Complex Parameter Tuning- Sensitivity to Parameters, Lack of Standardization
• Limited Theoretical Framework- Lack of Formal Guarantees
• Difficulty in Predicting Behavior
• Hardware Constraints
• Sensitivity to Environmental Changes
• Challenges with Unstructured Environments
• Ethical Use of Swarm Technology

15. F U T U R E O F S W A R M I N T E L L I G E N C E
• IoT and Smart Cities - Decentralized IoT Coordination
• Wildlife Monitoring and Conservation
• Pollution Control and Disaster Management
• Cloud-Based Swarm Simulations
• Ethical Frameworks for Swarm Intelligence
• Regulation and Governance
• Autonomous Surveillance

16. A N Y Q U E S T I O N S ?

17. T H A N K Y O U S O M U C H F O R L I S T E N I N G
U S