Swarm intelliengence

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This document discusses swarm intelligence, which refers to the collective behavior of decentralized and self-organized systems. It provides examples of swarm intelligence in ant colonies, where colonies are able to complete complex tasks through simple local interactions between individuals. The document also describes how positive feedback loops allow for the emergence of intelligent group behavior without centralized control, as seen in ants choosing the shortest path to food through pheromone signaling. Finally, it discusses potential applications of swarm intelligence concepts in areas like robotics, scheduling, and telecommunications.

BY
R.SUBHASHINI –II Yr
MANGAIYARKARASI ARTS &
SCIENCE COLLEGE FOR WOMEN

•The emergent collective intelligence of
groups of simple agents.
•Swarm intelligence seems not to be an
accident but rather a property of a
variety of systems

 identification of analogies: in swarm biology and IT
system.
 understanding: computer modelling of realistic
swarm biology
 engineering: model simplification and tuning for IT
applications

 ants solve complex tasks by simple
local means
 ant productivity is better than the
sum of their single activities
 ants are ‘grand masters’ in search and
exploitation

2 ants start with equal probability of going on either
path.

The ant on shorter path has a shorter to-and-
fro time from it’s nest to the food.

The density of pheromone on the shorter path is
higher because of 2 passes by the ant (as
compared to 1 by the other).

Over many iterations, more ants begin using the path
with higher pheromone, thereby further reinforcing it.

After some time, the shorter path is almost exclusively used.

‘Self-organization is a set of
dynamical mechanisms
whereby structures appear at the
global level of a
system from interactions of its
lower-level components

Robotics / Artificial Intelligence
 Process optimization / Staff
scheduling
 Telecommunication companies
 Entertainment

 biology makes compromises between
different goals
 biology sometimes fails
 some natural mechanisms are not well
understood
 well-defined problems can be solved by
better means

self-organization is based on:
 activity amplification by positive
feedback
 activity balancing by negative feedback
 amplification of random fluctuations
 multiple interactions

stigmergy - stimulation by work - is
based on:
 work as behavioural response to the
environmental state
 an environment that serves as a work
state memory
 work that does not depend on specific
agents

 Provide heuristic to solve difficult
problems
 Has been applied to wide variety of
applications
 Can be used in dynamic applications

Swarm intelligence refers to the collective behavior that emerges from decentralized, self-organized systems, both natural and artificial. In nature, it can be seen in the ability of ant colonies and bird flocks to coordinate and complete tasks through simple local interactions between individuals. Artificial swarm intelligence systems are distributed systems of interacting autonomous agents that coordinate through self-organization to solve problems through cooperation and division of labor. Examples of algorithms inspired by swarm intelligence include ant colony optimization and particle swarm optimization.

Swarm intelligence

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Jyotishkar dey roll 36.(swarm intelligence)

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Swarm intelligence is inspired by collective behavior of social insects like ants and bees. It involves developing algorithms for problem solving using decentralized and self-organized agents. Some examples of swarm intelligence include ant colony optimization and bee algorithms. Ant colony optimization works by simulating the pheromone trails left by ants to find shortest paths. The bee algorithm is based on how bees communicate through waggle dances to efficiently locate pollen sources. Swarm intelligence has applications in robotics, communication networks, and mobile ad-hoc networks. While it offers advantages like scalability and robustness, it also has disadvantages such as unknown convergence times and potential for stagnation.

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This document discusses swarm intelligence, which is inspired by natural phenomena like bird flocking and ant foraging behavior. It describes how swarms exhibit complex behavior through individuals following simple rules without centralized control. Two swarm algorithms are covered: ant colony optimization, which is applied to the traveling salesman problem, and the bee algorithm. The document compares these algorithms and discusses applications of swarm intelligence concepts.

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Swarm intelligence is the collective behavior of decentralized, self-organized systems, whether natural or artificial. It is used in artificial intelligence research. A swarm of robots could work similarly to an ant colony, with each following simple rules leading to self-organization and task completion without direct communication. Researchers have used swarms of simple robots to spell words and play piano through position-based algorithms. Swarm intelligence is also applied to fields like robotics, staff scheduling, and entertainment.

Vancea vasile swarm intelligence

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The document discusses swarm intelligence and several algorithms inspired by it, including ant colony optimization, particle swarm optimization, and stochastic diffusion search. It provides examples of how each algorithm works, modeling the decentralized and self-organized behavior of swarms in nature. It also mentions related metaheuristic optimization techniques like genetic algorithms, simulated annealing, and tabu search.

This document discusses swarm intelligence, which is an artificial intelligence technique inspired by the collective behavior of decentralized, self-organized systems found in nature, such as bird flocking, ant colonies, bee swarms, and fish schooling. The key principles of swarm intelligence are that there is no central control, agents follow simple rules, and emergent intelligence arises from the interactions between agents. Two commonly used swarm intelligence algorithms are ant colony optimization, inspired by how ants find food sources, and particle swarm optimization, inspired by the flocking behavior of birds. Swarm intelligence techniques have various applications in areas like robotics, engineering, telecommunications, and more.

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Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, the discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals. Some human artifacts also fall into the domain of swarm intelligence, notably some multi-robot systems, and also certain computer programs that are written to tackle optimization and data analysis problems. Taxonomy of Swarm Intelligence Swarm intelligence has a marked multidisciplinary character since systems with the above mentioned characteristics can be observed in a variety of domains. Research in swarm intelligence can be classified according to different criteria. Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied. Scientific vs. Engineering: An alternative and somehow more informative classification of swarm intelligence research can be given based on the goals that are pursued: we can identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems and to single out and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that are able to solve problems of practical relevance. The two dichotomies natural/artificial and scientific/engineering are orthogonal: although the typical scientific investigation concerns natural systems and the typical engineering application concerns the development of an artificial system, a number of swarm intelligence.Natural/Scientific: Foraging Behavior of Ants In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back. Artificial/Scientific: Clustering by a Swarm of Robots Several ant species cluster corpses to form cemeteries.

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This document discusses swarm intelligence and how it can be used to design algorithms. It provides examples of how ants exhibit swarm intelligence through their collective foraging behaviors without centralized control. Specifically, it mentions how ant colony algorithms have been designed and applied to solve optimization problems like the traveling salesman problem by simulating the indirect communication of ants through pheromone trails. The document also notes some potential applications of swarm intelligence in robotics and communication networks.

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Swarm intelligence is inspired by the collective behavior of social insects like ants and bees. It involves the decentralized control of groups of simple agents interacting locally with each other and their environment. Some key points covered: - Particle swarm optimization and ant colony optimization are two popular swarm intelligence algorithms. They are inspired by bird flocking and ant foraging behavior respectively. - PSO mimics social interaction to optimize problems. Each agent updates its position based on its own experience and neighboring agents. ACO uses simulated ants depositing pheromones to probabilistically construct solutions. - Swarm intelligence systems are robust, relatively simple, and can achieve complex functions through self-organization and emergence. They have applications in routing

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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.

UiPath Test Automation using UiPath Test Suite series, part 5

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UiPath Test Automation using UiPath Test Suite series, part 5

Swarm intelliengence

1. BY R.SUBHASHINI –II Yr MANGAIYARKARASI ARTS & SCIENCE COLLEGE FOR WOMEN

2. •The emergent collective intelligence of groups of simple agents. •Swarm intelligence seems not to be an accident but rather a property of a variety of systems

4.  identification of analogies: in swarm biology and IT system.  understanding: computer modelling of realistic swarm biology  engineering: model simplification and tuning for IT applications

5.  ants solve complex tasks by simple local means  ant productivity is better than the sum of their single activities  ants are ‘grand masters’ in search and exploitation

6. 2 ants start with equal probability of going on either path.

7. The ant on shorter path has a shorter to-and- fro time from it’s nest to the food.

8. The density of pheromone on the shorter path is higher because of 2 passes by the ant (as compared to 1 by the other).

9. The next ant takes the shorter route.

10. Over many iterations, more ants begin using the path with higher pheromone, thereby further reinforcing it.

11. After some time, the shorter path is almost exclusively used.

12. ‘Self-organization is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions of its lower-level components

13. Robotics / Artificial Intelligence  Process optimization / Staff scheduling  Telecommunication companies  Entertainment

14.

15.

16.  biology makes compromises between different goals  biology sometimes fails  some natural mechanisms are not well understood  well-defined problems can be solved by better means

17. self-organization is based on:  activity amplification by positive feedback  activity balancing by negative feedback  amplification of random fluctuations  multiple interactions

18. stigmergy - stimulation by work - is based on:  work as behavioural response to the environmental state  an environment that serves as a work state memory  work that does not depend on specific agents

19.  Provide heuristic to solve difficult problems  Has been applied to wide variety of applications  Can be used in dynamic applications

20. ANY QUERIES

Swarm intelliengence

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