Presentation on theme: "Swarm Intelligence An Introduction"— Presentation transcript:
1 Swarm Intelligence An Introduction Applied Artificial Intelligence(Third Semester, ME in CSN/CIE)
2 A loosely structured collection of interacting agents What is Swarm?A loosely structured collection of interacting agentsAgents:Individuals that belong to a group (but are not necessarily identical)They contribute to and benefit from the groupThey can recognize, communicate, and/or interact with each otherThe instinctive perception of swarms is a group of agents in motion – but that does not always have to be the case.A swarm is better understood if thought of as agents exhibiting a collective behavior
3 Swarm Intelligence (SI) is an Artificial Intelligence (AI) technique based on the collective behaviour in decentralized, self-organized systems.Generally made up of agents who interact with each other and the environment.No centralized control structures.Based on group behaviour found in nature.
5 Honey Bee SwarmAnd thy Lord taught the Bee to build its cells in hills, on trees, and in (men’s) habitations; Then to eat of all the produce (of the earth), and find with skill the spacious paths of its Lord: there issues from within their bodies a drink of varying colors, wherein is healing for men: verily in this is a Sign for those who give thought” [Quran, 16, 68-69]"And your Lord revealed to the bee: 'Build dwellings in the mountains and the trees and also in the structures which men erect. Then eat from every kind of fruit and travel the paths of your Lord, which have been made easy for you to follow.' From inside them comes a drink of varying colors, containing healing for mankind. There is certainly a sign in that for people who reflect." [an-Nahl: 68-69]
6 Examples from NatureAnt Colony: Until, when they came upon the valley of the ants, an ant said, “O ants, enter your homes so that you do not be crushed by Solomon and his soldiers while they do not feel it [Quran 27:18]
7 Examples from NatureFlocks of Birds: “Do they not look at the birds, held poised in the midst of (the air and) the sky? Nothing holds them up but (the power of) Allah. Verily in this are Signs for those who believe.”Do they not observe the birds above them, spreading their wings and folding them in? None can uphold them except the Most Gracious: Truly it is He that watches over all things. (Verse 67:19)
9 Examples from NatureImmune System: "He is Supreme over His creatures, and He appoints guards to protect you." [Quran 6:61]Immune System is a group of cells, molecules, and organs that act together to defend the body against foreign invaders that may cause disease, such as bacteria, virus, and fungi.
10 Examples from NatureLocusts: They will emerge from their graves with downcast eyes, like swarming locusts. (Qur'an, 54:7)
12 HistoryIn the 1950s, scientists started to conduct research about how social animals and insects such as bees, ants, termites, etc. are communicating and coordinating themselves.In 1973, “Actor Model” was developed by Carl Hewitt, Peter Bishop und Richard Steiger. The Actor model is a mathematical model of concurrent computation that treats "actors" as the universal primitives of concurrent digital computation: in response to a message that it receives, an actor can make local decisions, create more actors, send more messages, and determine how to respond to the next message received. In 1989, Beni and Wang in 1989 introduced Cellular Robotic Systems.The concept of SI was expanded by Bonabeau, Dorigo, and Theraulaz in 1999.
14 Ant Colony Optimization (ACO) The study of artificial systems modeled after the behavior of real ant colonies and are useful in solving discrete optimization problemsIntroduced in 1992 by Marco DorigoOriginally called it the Ant System (AS)Has been applied toTraveling Salesman Problem (and other shortest path problems)Several NP-hard ProblemsIt is a population-based metaheuristic used to find approximate solutions to difficult optimization problems
15 What is Metaheuristic?A metaheuristic refers to a master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality.Or more simply:It is a set of algorithms used to define heuristic methods that can be used for a large set of problems
16 A set of software agents Stochastic Based on the pheromone model Artificial AntA set of software agentsStochasticBased on the pheromone modelPheromones are used by real ants to mark paths. Ants follow these paths (i.e., trail-following behaviors)Incrementally build solutions by moving on a graphConstraints of the problem are built into the heuristics of the ants
17 Using ACOThe optimization problem must be written in the form of a path finding problem with a weighted graphThe artificial ants search for “good” solutions by moving on the graphAnts can also build infeasible solutions – which could be helpful in solving some optimization problemsThe metaheuristic is constructed using three procedures:ConstructAntsSolutionsUpdatePheromonesDaemonActions
18 ConstructAntsSolutions Manages the colony of antsAnts move to neighboring nodes of the graphMoves are determined by stochastic local decision policies based on pheromone tails and heuristic informationEvaluates the current partial solution to determine the quantity of pheromones the ants should deposit at a given node
19 UpdatePheromones Process for modifying the pheromone trails Modified byIncreaseAnts deposit pheromones on the nodes (or the edges)DecreaseAnts don’t replace the pheromones and they evaporateIncreasing the pheromones increases the probability of paths being used (i.e., building the solution)Decreasing the pheromones decreases the probability of the paths being used (i.e., forgetting)
20 Used to implement larger actions that require more than one ant DaemonActionsUsed to implement larger actions that require more than one antExamples:Perform a local searchCollection of global information
21 Particle Swarm Optimization (PSO) A population based stochastic optimization techniqueSearches for an optimal solution in the computable search spaceDeveloped in 1995 by Eberhart and KennedyInspiration: Swarms of Bees, Flocks of Birds, Schools of Fish
22 Each PSO individual has the ability to remember More on PSOIn PSO individuals strive to improve themselves and often achieve this by observing and imitating their neighborsEach PSO individual has the ability to rememberPSO has simple algorithms and low overheadMaking it more popular in some circumstances than Genetic/Evolutionary AlgorithmsHas only one operation calculation:Velocity: a vector of numbers that are added to the position coordinates to move an individual
23 Psychological System in PSO A psychological system can be thought of as an “information-processing” functionYou can measure psychological systems by identifying points in psychological spaceUsually the psychological space is considered to be multidimensional.
24 Philosophical Leaps” Required Individual minds = a point in spaceMultiple individuals can be plotted in a set of coordinatesMeasuring the individuals result in a “population of points”Individuals near each other imply that they are similarSome areas of space are better than others
25 Applying Social Psychology Individuals (points) tend toMove towards each otherInfluence each otherWhy?Individuals want to be in agreement with their neighborsIndividuals (points) are influenced by:Their previous actions/behaviorsThe success achieved by their neighbors
26 WHAT HAPPENS IN PSOIndividuals in a population learn from previous experiences and the experiences of those around themThe direction of movement is a function of:Current positionVelocity (or in some models, probability)Location of individuals “best” successLocation of neighbors “best” successesTherefore, each individual in a population will gradually move towards the “better” areas of the problem spaceHence, the overall population moves towards “better” areas of the problem space
27 Performance of the PSO Algorithm Relies on selecting several parameters correctlyParameters:Constriction factorUsed to control the convergence properties of a PSOInertia weightHow much of the velocity should be retained from previous stepsCognitive parameterThe individual’s “best” success so farSocial parameterNeighbors’ “best” successes so farVmaxMaximum velocity along any dimension
28 Artificial Bee Colony Algorithm (ABC) This is a population based search algorithm first developed in 2005.It mimics the food foraging behaviour of honey bees.A colony of honey bees can extend itself over long distances (up to 14 km) and in multiple directions simultaneously to exploit a large number of food sources. A colony prospers by deploying its foragers to good fields. In principle, flower patches with plentiful amounts of nectar or pollen that can be collected with less effort should be visited by more bees, whereas patches with less nectar or pollen should receive fewer bees.Colony Communication contains three pieces of information regarding a flower patch:1. The direction in which it will be found Its distance from the hive Its quality rating (fitness).
29 Pseudo Code for ABC Initialize population with random solutions. Evaluate fitness of the population.While (stopping criterion not met) //Forming new population.Select sites for neighbourhood search.Recruit bees for selected sites (more beesfor best sites) and evaluate fitnesses.Select the fittest bee from each patch.Assign remaining bees to search randomlyand evaluate their fitnesses.End While.
30 Advantages of SIThe systems are scalable because the same control architecture can be applied to a couple of agents or thousands of agentsThe systems are flexible because agents can be easily added or removed without influencing the structureThe systems are robust because agents are simple in design, the reliance on individual agents is small, and failure of a single agents has little impact on the system’s performanceThe systems are able to adapt to new situations easily
31 Recent Developments in SI Applications U.S. Military is applying SI techniques to control of unmanned vehiclesNASA is applying SI techniques for planetary mappingMedical Research is trying SI based controls for nanobots to fight cancerSI techniques are applied to load balancing in telecommunication networksEntertainment industry is applying SI techniques for battle and crowd scenes
32 AssignmentWrite a technical report on the applicability of Swarm intelligence in the following areas:Data MiningIntelligent RobotsRoutingMovies/Entertainment IndustryParameter OptimizationProtection/Security SystemsApplications of ABC algorithmYour report should include a comprehensive literature review with a list of references.