Swarm Intelligence
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Computer Science Clay
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01-03-2009, 10:48 AM

Swarm Intelligence

Swarm Intelligence is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. The expression swarm intelligence was introduced by Beni & Wang in 1989, in the context of cellular robotic systems.

SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behavior. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.

Application of swarm principles to large numbers of robots is called as swarm robotics
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Swarm Intelligence
Presented by:
Michael Jacob Mathew
College Of Engineering, Trivandrum
2007-11 batch

.pptx   Swarm Intelligence.pptx (Size: 921.9 KB / Downloads: 139)
hi guys, If you like this presentation, please thank the author at: michaeljacob.mathew@gmail.com


The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems

Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial, where collective behavior of simple agents causes coherent solutions or patterns to emerge. The concept is employed in work on artificial intelligence.

Social Insects





Eg:Bees, Wasps, Termites, Ants

An In-depth Look at Real Ant Behaviour
Interrupt The Flow
The Path Thickens!
The New Shortest Path
Possible Solutions to Create Swarm Intelligence Systems
Create a catalog of the collective behaviors

Model how social insects collectively perform tasks

Use this model as a basis upon which artificial variations can be developed

Model parameters can be tuned within a biologically relevant range or by adding non-biological factors to the model

Four Ingredients of Self Organization

Positive Feedback

Negative Feedback

Amplification of Fluctuations - randomness

Reliance on multiple interactions
Properties of Self-Organization
Creation of structures
Nest, foraging trails, or social organization
Changes resulting from the existence of multiple paths of development
Non-coordinated & coordinated phases
Possible coexistence of multiple stable states
Two equal food sources
Types of Interactions For Social Insects
Direct Interactions

Food/liquid exchange, visual contact, chemical contact (pheromones)
Indirect Interactions (Stigmergy)

Individual behavior modifies the environment, which in turn modifies the behavior of other individuals

Stigmergy Example

Pillar construction in termites

Ants  Agents

Stigmergy can be operational

Coordination by indirect interaction is more appealing than direct communication

Stigmergy reduces (or eliminates) communications between agents
From Ants to Algorithms

Swarm intelligence information allows us to address modeling via:

Problem solving


Real world applications


Observe Phenomenon

Create a biologically motivated model

Explore model without constraints


Creates a simplified picture of reality

Observable relevant quantities become variables of the model

Other (hidden) variables build connections
A Good Model has...

Parameter values correspond to values of their natural counterparts

Collective task completion

No need for overly complex algorithms

Adaptable to changing environment

Communication Networks

Routing packets to destination in shortest time

Similar to Shortest Route (using DIP)

Statistics kept from prior routing (learning from experience)

Ant Net Algorithm:


Shortest Route
Fault Tolerance
Problems Regarding Swarm Intelligent Systems
Swarm Intelligent Systems are hard to ‘program’ since the problems are usually difficult to define

Solutions are emergent in the systems

Solutions result from behaviors and interactions among and between individual agents
Closing Arguments

Still very theoretical
No clear boundaries
Details about inner workings of insect swarms

The future……??
Pipe Inspection
Self-Assembling Robots
Engine Maintenance
Cleaning Ship Hulls
Satellite Maintenance
Pest Eradication


Multi-resolution Ant Colony - A New Approach to Use Swarm Intelligence in Continuous Problems
By: Rezaee, A. Jalali, M.J.P. Buinzahra Branch, Islamic Azad Univ., Buinzahra, Iran
This paper appears in: Information and Multimedia Technology, 2009. ICIMT '09. International Conference on Issue Date : 16-18 Dec. 2009 Date of Current Version : 15 January 2010

Different Types of Swarm Intelligence Algorithm for Routing
By: Sharvani, G.S. Cauvery, N.K. Rangaswamy, T.M. R.V. Coll. Of Eng., Bangalore, India
This paper appears in: Advances in Recent Technologies in Communication and Computing, 2009. ARTCom ‘09. International Conference on Issue Date : 27-28 Oct. 2009 Date of Current Version : 17 November 2009

Optimization of integrated circuits using an artificial intelligence algorithm
By:Vural, R.A. Yildirim, T. Commun. Eng. Dept., Yildiz Tech. Univ. Electron., Istanbul
This paper appears in: Research in Microelectronics and Electronics, 2008. PRIME 2008. Ph.D. Issue Date : June 22 2008-April 25 2008 Date of Current Version : 12 August 2008

Swarm Intelligence From Natural to Artificial Systems
By:Eric Bonabeau
Marco Dorigo
Guy Theraulaz

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