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
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO)
based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO
has been inspired from natural ants system, their behavior, team coordination, synchronization for the
searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as
a leading metaheuristic technique for the solution of combinatorial optimization problems which can be
used to find shortest path through construction graph. This paper describe about various behavior of ants,
successfully used ACO algorithms, applications and current trends. In recent years, some researchers
have also focused on the application of ACO algorithms to design of wireless communication network,
bioinformatics problem, dynamic problem and multi-objective problem.
Swarm Intelligence: An Application of Ant Colony OptimizationIJMER
Swarm intelligence, a branch of artificial intelligence is a part which discusses the collective
behaviour of social animals such as ants, fishes, termites, birds, bacteria. The collective behaviour of
animals to achieve target can be used in practical applications. One of the applications is ant colony
optimization. Ongoing research of ACO, there are diverse applications namely data mining, image
processing, power electronic circuit design etc. One of that is network routing. By using ACO, we can
find the shortest path in network routing
Performance Evaluation of Different Network Topologies Based On Ant Colony Op...ijwmn
All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile node. The problem remains the same, in order to get the best from the network; there is a need to find the shortest path. The more complicated the network is, the more difficult it is to manage the routes and indicate which one is the best. The Nature gives us a solution to find the shortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only to explore a vast area, but also to indicate to their peers the location of the food while bringing it back to the nest. Most of the time, they will find the shortest path and adapt to ground changes, hence proving their great efficiency toward this difficult task. The purpose of this paper is to evaluate the performance of different network topologies based on Ant Colony Optimization Algorithm. Simulation is done in NS-2.
A Multi-Objective Ant Colony System Algorithm for Virtual Machine PlacementIJERA Editor
Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
Comparative Study of Ant Colony Optimization And Gang SchedulingIJTET Journal
Abstract— Ant Colony Optimization (ACO) is a well known and rapidly evolving meta-heuristic technique. All optimization problems have already taken advantage of the ACO technique while countless others are on their way. Ant Colony Optimization (ACO) has been used as an effective algorithm in solving the scheduling problem in grid computing. Whereas gang scheduling is a scheduling algorithm that is used to schedule the parallel systems and schedules related threads or processes to run simultaneously on different processors. The threads that are scheduled are belonging to the same process, but they from different processes in some cases, for example when the processes have a producer-consumer relationship, when all processes come from the same MPI program.
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Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO)
based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO
has been inspired from natural ants system, their behavior, team coordination, synchronization for the
searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as
a leading metaheuristic technique for the solution of combinatorial optimization problems which can be
used to find shortest path through construction graph. This paper describe about various behavior of ants,
successfully used ACO algorithms, applications and current trends. In recent years, some researchers
have also focused on the application of ACO algorithms to design of wireless communication network,
bioinformatics problem, dynamic problem and multi-objective problem.
Swarm Intelligence: An Application of Ant Colony OptimizationIJMER
Swarm intelligence, a branch of artificial intelligence is a part which discusses the collective
behaviour of social animals such as ants, fishes, termites, birds, bacteria. The collective behaviour of
animals to achieve target can be used in practical applications. One of the applications is ant colony
optimization. Ongoing research of ACO, there are diverse applications namely data mining, image
processing, power electronic circuit design etc. One of that is network routing. By using ACO, we can
find the shortest path in network routing
Performance Evaluation of Different Network Topologies Based On Ant Colony Op...ijwmn
All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile node. The problem remains the same, in order to get the best from the network; there is a need to find the shortest path. The more complicated the network is, the more difficult it is to manage the routes and indicate which one is the best. The Nature gives us a solution to find the shortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only to explore a vast area, but also to indicate to their peers the location of the food while bringing it back to the nest. Most of the time, they will find the shortest path and adapt to ground changes, hence proving their great efficiency toward this difficult task. The purpose of this paper is to evaluate the performance of different network topologies based on Ant Colony Optimization Algorithm. Simulation is done in NS-2.
A Multi-Objective Ant Colony System Algorithm for Virtual Machine PlacementIJERA Editor
Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
Comparative Study of Ant Colony Optimization And Gang SchedulingIJTET Journal
Abstract— Ant Colony Optimization (ACO) is a well known and rapidly evolving meta-heuristic technique. All optimization problems have already taken advantage of the ACO technique while countless others are on their way. Ant Colony Optimization (ACO) has been used as an effective algorithm in solving the scheduling problem in grid computing. Whereas gang scheduling is a scheduling algorithm that is used to schedule the parallel systems and schedules related threads or processes to run simultaneously on different processors. The threads that are scheduled are belonging to the same process, but they from different processes in some cases, for example when the processes have a producer-consumer relationship, when all processes come from the same MPI program.
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Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
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2. Swarming – The Definition
aggregation of similar animals, generally
cruising in the same direction
Termites swarm to build colonies
Birds swarm to find food
Bees swarm to reproduce
3. Why do animals swarm?
To forage better
To migrate
As a defense against predators
Social Insects have survived for millions of
years.
9. Swarming - Characteristics
Simple rules for each individual
No central control
Decentralized and hence robust
Emergent
Performs complex functions
10. Learn from insects
Computer Systems are getting complicated
Hard to have a master control
Swarm intelligence systems are:
Robust
Relatively simple
11. Swarm Intelligence - Definition
“any attempt to design algorithms or
distributed problem-solving devices inspired
by the collective behavior of social insect
colonies and other animal societies”
[Bonabeau, Dorigo, Theraulaz: Swarm
Intelligence]
Solves optimization problems
15. Particle Swarm Optimization
Particle swarm optimization imitates human
or insects social behavior.
Individuals interact with one another while
learning from their own experience, and
gradually move towards the goal.
It is easily implemented and has proven both
very effective and quick when applied to a
diverse set of optimization problems.
16. Bird flocking is one of the best example of
PSO in nature.
One motive of the development of PSO was
to model human social behavior.
17. Applications of PSO
Neural networks like Human tumor analysis,
Computer numerically controlled milling
optimization;
Ingredient mix optimization;
Pressure vessel (design a container of
compressed air, with many constraints).
Basically all the above applications fall in a
category of finding the global maxima of a
continuous, discrete, or mixed search space,
with multiple local maxima.
18. Algorithm of PSO
Each particle (or agent) evaluates the
function to maximize at each point it visits in
spaces.
Each agent remembers the best value of the
function found so far by it (pbest) and its co-
ordinates.
Secondly, each agent know the globally best
position that one member of the flock had
found, and its value (gbest).
19. Algorithm – Phase 1 (1D)
Using the co-ordinates of pbest and gbest,
each agent calculates its new velocity as:
vi = vi + c1 x rand() x (pbestxi – presentxi)
+ c2 x rand() x (gbestx – presentxi)
where 0 < rand() <1
presentxi = presentxi + (vi x Δt)
26. Ant Colony Optimization - Biological
Inspiration
Inspired by foraging behavior of ants.
Ants find shortest path to food source from nest.
Ants deposit pheromone along traveled path which
is used by other ants to follow the trail.
This kind of indirect communication via the local
environment is called stigmergy.
Has adaptability, robustness and redundancy.
27. Foraging behavior of Ants
2 ants start with equal probability of going on
either path.
28. Foraging behavior of Ants
The ant on shorter path has a shorter to-and-
fro time from it’s nest to the food.
29. Foraging behavior of Ants
The density of pheromone on the shorter
path is higher because of 2 passes by the ant
(as compared to 1 by the other).
31. Foraging behavior of Ants
Over many iterations, more ants begin using
the path with higher pheromone, thereby
further reinforcing it.
32. Foraging behavior of Ants
After some time, the shorter path is almost
exclusively used.
33. Generic ACO
Formalized into a metaheuristic.
Artificial ants build solutions to an
optimization problem and exchange info on
their quality vis-à-vis real ants.
A combinatorial optimization problem reduced
to a construction graph.
Ants build partial solutions in each iteration
and deposit pheromone on each vertex.
34. Ant Colony Metaheuristic
ConstructAntSolutions: Partial solution extended by adding
an edge based on stochastic and pheromone
considerations.
ApplyLocalSearch: problem-specific, used in state-of-art
ACO algorithms.
UpdatePheromones: increase pheromone of good
solutions, decrease that of bad solutions (pheromone
evaporation).
35. Various Algorithms
First in early 90’s.
Ant System (AS):
First ACO algorithm.
Pheromone updated by all ants in the iteration.
Ants select next vertex by a stochastic function
which depends on both pheromone and problem-
specific heuristic nij = 1
dij
36. Various Algorithms - 2
MAX-MIN Ant System (MMAS):
Improves over AS.
Only best ant updates pheromone.
Value of pheromone is bound.
Lbest is length of tour of best ant.
Bounds on pheromone are problem specific.
37. Various Algorithms - 3
Ant Colony System (ACS):
Local pheromone update in addition to offline
pheromone update.
By all ants after each construction step only to last
edge traversed.
Diversify search by subsequent ants and produce
different solutions in an iteration.
Local update:
Offline update:
38. Theoretical Details
Convergence to optimal solutions has been
proved.
Can’t predict how quickly optimal results will
be found.
Suffer from stagnation and selection bias.
40. Ant like agents for routing
Intuitive to think of ants for routing problem
Aim is to get shortest path
Start as usual
Release a number of ants from source, let the age
of ant increases with increase in hops
decide on pheromone trails i.e biasing the entries in
routing table in favor of youngest ant
Problem – Ants at an node do not know the
path to destiation, can't cahnge table entry
41. Routing continued ...
Possible Solutions
first get to dest. and then retrace
Needs memory to store the path
And intelligence to revert the path
Leave unique entries on nodes
a lot of entries at every node
Observation – At any intermediate node, ant
knows the path to source from that node.
now leave influence on routing table having entry
“route to source via that link”
42. Routing contd ...
Now at any node it has information about
shortest path to dest., left by ants from dest.
The ant following shortest path should have
maximum influence
A convenient form of pheromone can be
inverse of age + constant
The table may get frozen, with one entry
almost 1, add some noise f i.e probabilty that
an ant choses purely random path
43. Dealing with congestion
Add a function of degree of congestion of each
node to age of an ant
Delay an ant at congested node, this prevents
ants from influencing route table
44. SI - Limitations
Theoretical analysis is difficult, due to
sequences of probabilistic choices
Most of the research are experimental
Though convergence in guaranteed, time to
convergence is uncertain
45. Scope
Startup !!
Bluetronics, Smartintel
Analytic proof and models of swarm based
algorithm remain topics of ongoing research
List of applications using SI growing fast
Controlling unmanned vehicles.
Satellite Image Classification
Movie effects
46. Conclusion
Provide heuristic to solve difficult problems
Has been applied to wide variety of
applications
Can be used in dynamic applications
47. References
Reynolds, C. W. (1987) Flocks, Herds, and Schools: A Distributed Behavioral
Model, in Computer Graphics, 21(4) (SIGGRAPH '87 Conference
Proceedings) pages 25-34.
James Kennedy, Russell Eberhart. Particle Swarm Optimization, IEEE Conf.
on Neural networks – 1995
www.adaptiveview.com/articles/ ipsop1
M.Dorigo, M.Birattari, T.Stutzle, Ant colony optimization – Artificial Ants as a
computational intelligence technique, IEEE Computational Intelligence
Magazine 2006
Ruud Schoonderwoerd, Owen Holland, Janet Bruten - 1996. Ant like agents
for load balancing in telecommunication networks, Adaptive behavior, 5(2).