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SWARM INTELLIGENCE FOR LOCAL TRANSIT
-AMIT DHAKATE (V SEM., SEC.- A, R.NO.-21)-PROJECT GUIDEPROF. V.M. KORDE
CONTENTS• Introduction: What is swarm intelligence• Background• Swarm intelligence • Multi-agent system(MAS)• Naturally occurring algorithms• Ant colony optimization• Energy Balancing Routing
in Sensor Network• Energy Balancing Routing
in Sensor Network• Particle Swarm Optimization• Conclusion
IntroductionSwarm intelligence is based on collective
behavior of a self-organized group of agents.The problem that is being addressed in this
seminar is transportation.This goal has two parts:
• The swarm will start at the home base & will first need to find the goal, identify it & position it path to its proper location.
• After an agent in the swarm has succeeded in finding the goal base, it gives a signal to other agent in the grid other agent finds the second goal of transporting all supplies in the home base to the goal base.
Background• Multi-agent systems and swarm intelligence were first
introduced by G. Beni with cellular robotics.• Swarm intelligence is one approach to solving problems
by imitating nature and, more specifically, by imitating swarms such as bees, ants, and birds.
• The significance of swarm intelligence is that each agent in the swarm is governed by a simple set of rules, but the entire swarm as a whole exhibits complex behavior.
• This also usually means that each agent does not have global knowledge of its environment nor does it have access to all knowledge of the swarm.
• It acts based on local environmental knowledge and nearby neighbors only.
Swarm Intelligence• There are a number of systems which seems
to exhibit swarm intelligence:− Animal colonies and specifically insect
colonies like ants, termites, and bees− Bacteria which appear able to act in a
finalized way− The Brain: intelligence and mind arises
form the interaction of simple neurons− The Cell: homeostasis and the capability
of adapting and reproducing ariseform protein interactions
• Swarm intelligence is not an “accident” but rather a property of the systems
Characteristics of swarm intelligent systems is often
said to be an “emergent behavior”
•− It does not arise from a rationale choice•− It does not arise from an engineering finalized analysis•− No one and nothing in the system says: I will do that because this will lead to a specific behavior of the system•− The individual in the system have no global perspective•− They are not aware of what’s globally happening•− They are not aware they are doing something intelligent
Multi-agent systems(MAS)The agents in a multi-agent system have several
important characteristics:• Autonomy: the agents are at least partially autonomous.• Local views: no agent has a full global view of the system,
or the system is too complex for an agent to make practical use of such knowledge.
• Decentralization: there is no designated controlling agent (or the system is effectively reduced to a monolithic system.
Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots[6], humans or human teams. A multi-agent system may contain combined human-agent teams.
Naturally occurring algorithms• Ant colony optimization (ACO) is a class of optimization
algorithms modeled on the actions of an ant colony. ACO methods are useful in problems that need to find paths to goals. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions.
• Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space.
• Stochastic diffusion search (SDS) is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterized by the agent's current hypothesis.
Naturally occurring algorithms(contd.)• Gravitational search algorithm (GSA) is
constructed based on the law of Gravity and the notion of mass interactions. The GSA algorithm uses the theory of Newtonian physics and its searcher agents are the collection of masses.
• Intelligent Water Drops algorithm (IWD) is a swarm-based nature-inspired optimization algorithm, which has been inspired from natural rivers and how they find almost optimal paths to their destination.These near optimal or optimal paths follow from actions and reactions occurring among the water drops and the water drops with their riverbeds.
Ant colony optimization
Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment to fold protein or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations.
The general algorithm is relatively simple and based on a set of ants, each making one of the possible round-trips along the cities. At each stage, the ant chooses to move from one city to another according to some rules:
1. It must visit each city exactly once;2. A distant city has less chance of being chosen (the visibility);3. The more intense the pheromone trail laid out on an edge
between two cities, the greater the probability that that edge will be chosen;
4. Having completed its journey, the ant deposits more pheromones on all edges it traversed, if the journey is short;
5. After each iteration, trails of pheromones evaporate.
Ant routing algorithm in AntColony Optimization
Ant agents are launched into a network, each
agent going from a source to a
destination node.
The ant agent maintains a list of visited nodes and
the time elapsed to arrive there and utilizes that
information to find a way backwards(like ants do
using pheromone).
At each node, the data package will use the digital pheromone
value as the transiting probability
for deciding data moving route.
Energy Balancing Routingin Sensor Network
An ad-hoc wireless network can largely
reduce energy consumption in
data transmission.
Virtual pheromone trail generate: Artificial forward ant agents are
launched regularly to destinations in the network. It maintains a list
of visited nodes and the time elapsed to arrive there and utilizes
same information to get backwards.
Virtual pheromone maintenance: At each sensor
node, the transiting probability is used for deciding data moving
route. The data package will reduce the node’s pheromone
value when it pass by the node.
Particle Swarm Optimization• Particle Swarm Optimization (PSO) is a population
based stochastic optimization technique inspired by social behavior of bird flocking.PSO applies the concept of social interaction to problem solving.
• In PSO, A group of agents, “particles”, are thrown into the search space. The particles communicate either directly or indirectly with one another for search directions (gradients).
• PSO is a simple but powerful search technique. It has been applied successfully to a wide variety of search and optimization problems.
Particle Swarm Optimization (PSO)in Dynamic Environment• The conventional PSO is used to discover the optimal solution in a static
environment. Conventional PSO has poor tracking characteristics when the optimal solution is moving.
• When the environment is dynamic, the task of the optimization is not only to acquire the extreme but also to track the extreme's trajectory as closely as possible.
• A human social adaptive based PSO is used to discover and track the optimal solution in a dynamic environment.
• Each particle evaluates the knowledge it received from it previous experience and from the neighbor’s experience.
• The outdated knowledge will be gradually forget by the particle and new knowledge will be learned.
Application of Ant System for vehicle routing problem
• Capacitated vehicle routing problem (CVRP)
• Multi-depot vehicle routing problem (MDVRP)
• Period vehicle routing problem (PVRP)
• Split delivery vehicle routing problem (SDVRP)
• Stochastic vehicle routing problem (SVRP)
• Vehicle routing problem with pick-up and delivery (VRPPD)
• Vehicle routing problem with time windows (VRPTW)
• Time Dependent Vehicle Routing Problem with Time Windows (TDVRPTW)
Vehicle routing problem with pick-up and delivery (VRPPD)
•Automation in pickup and delivery automation is gaining importance.
•But routing problems are faced when one or vehicles share same arena.
Solution Swarm intelligence
ARENA
ALGORITHM FOR SOLVING ABOVE PROBLEM
Search for object
along path
get nr. The
boundary
Place the object to the
reqd. position
Wait until other robot
comes in
Continue search along path If no boxes
found return in inside zone
and STOP
Return to the position from
where object was taken
Check if other
robot is in or out
If object detecte
d
yes
no
yes no
Applications of Ant ColonyOptimization
Traffic on telecommunications systems, he internet, roads, rail, and sea would all benefit from the reduction incongestion that efficient routing algorithms could provide.
Southwest airlines are actually putting the ant colony research to work, with impressive payback
British Telecom Network Southwest Airlines Cargo System
Swarm Robotics Surveillance systems(misc)
Surveillance systems are often needed in areas too hostile or dangerous for a direct human presence. One major problem is the control and coordination of multiple cooperating robots.
We have looked to the distributed control strategies found in nature in the form of social insects as an inspiration for new control schemes for the coordination and control of large-scale distributed robotic systems.
The research goal is to manipulate the interactions of multiple small, low-cost robots, with a limited range of local communication ability, to collaboratively search and engage tasks in an unknown large-scale hostile area.
Satellite
Maintenance
The Future?Medical
Interacting Chips
in Mundane
Objects
Cleaning Ship
Hulls
Pipe Inspection
Pest Eradication
Engine Maintenance
TelecommunicationsSelf-Assembling
Robots
Job Scheduling
Vehicle Routing Data Clustering
Distr
ibut
ed M
ail
Syst
ems
Optim
al
Resource
AllocationCombinatorial
Optimization
Conclusion• Swarm Intelligence is a scientific theory inspired from the
emergent behaviors of ants, birds and other social animals.• Swarm Intelligence proposes a new way of thinking the
solution of the non-linear complex problems.• The emergent property of swarm based MAS demonstrates
a fundamentally important principle that has been beneficial to nature.
• We are constrained by linear thinking: it is hard for us to understand how all the various parts of the system interact and add up to the whole.
• It is very important to use swarm intelligence to understand how large-scale emergent patterns and behaviors can result from the actions and interactions of the individual components of a system.
Thank you