Swarm Intelligence

1. Swarm Intelligence Quantitative analysis: How to make a decision? Thank you for all referred pictures and information.

2. Agenda • Introduction • Nature of Swarm • Swarm Intelligence: Applying • Existing Algorithms and Applications

3. Introduction • A group of simple or complexindividuals can exhibit very complex emergent behavior • collective behavior • applies to many processes in nature, creating a useful concept in many contexts • collectively migrating bacteria • insects or birds, or phenomena where groups of organisms or non-living objects synchronize their signals or motion

4. Introduction • A collective behavior shows a seeming intelligence that far transcends the abilities of the members, named “swarm intelligence” • Decentralized system • requires multiple agents to make their own independent decisions • Self-organized system • it is not directed or controlled by any agent or subsystem inside or outside of the system

5. Decentralized System • There is no single centralized authority that makes decisions on behalf of all the agents. • Agent makes local autonomous decisions towards its individual goals which may possibly conflict with those of other agents. • Agents directly interact with each other and share information or provide service to other agents. http://www.isr.uci.edu/projects/pace/decentralization.html

6. Self-organized System • “In biological systems self-organization is a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system's components are executed using only local information, without reference to the global pattern” Camazine, Deneubourg, Franks, Sneyd, Theraulaz, Bonabeau, Self-Organization in Biological Systems, Princeton University Press, 2003.

7. Self-organized System • Examples • Coordination of human movement • Flocking behaviour • Creation of structures by social animals

8. Nature of Swarm • The word swarmconjures up images of large groups of small insects in which each member performs a simple role, but the action produces complex behavior as a whole. • Termites swarm to build colonies • Ants swarm to find food sources • Bees swarm to reproduce • Bird swarms • each bird tries to find another to fly with • flies slightly higher to one side to reduce drag, with the birds eventually forming a flock.

9. Categorizing Collective Behaviors • Coordination • Interactions between individuals generate synchronized and oriented movements of the individuals toward a specific goal. • Coordination is at work in most of the building activities in insect colonies. • Nest building in certain species of social insects http://science.howstuffworks.com/environmental/life/zoology/insects-arachnids/termite3.htm

10. Categorizing Collective Behaviors • Cooperation • Occurs when individuals achieve together a task that could not be done by a single one. • The individuals must combine their efforts in order to successfully solve a problem that goes beyond their individual abilities. • Bringking food back to the nest http://deepintoscripture.com/2012/05/01/in-which-there-are-ants-and-a-news-reporter-and-tournament-results/

11. Categorizing Collective Behaviors • Deliberation • Deliberation refers to mechanisms that occur when a colony faces several opportunities. • These mechanisms result in a collective choice for at least one of the opportunities. • Ants have discovered several food sources with different qualities or richness, or several paths that lead to a food source, they generally select only one of the different opportunities. • In this case, the deliberation is driven by the competition between the chemical trails leading to each opportunity http://www.responsiblepestcontrolmesa.com/argentine-ant-pest-control-exterminating/

12. Example Nature of Deliberation • For two food sources: Ant Colony • A rich food source far from the nest and an inferior food source close to the nest, some ants went to the inferior food source because it was near the nest, but some other ants wandered, and then they found the rich food source. • They emitted pheromone along the path from the nest to the rich food source until the trail to the rich food source was stronger than the original one. • Finally, the most of ants shift to the richer source. Therefore, the randomness of some ants’ behavior makes it possible to explore multiple food sources in parallel

13. Categorizing Collective Behaviors • Collaboration • Collaboration means that different activities are performed simultaneously by groups of specialized individuals • foraging for prey or tending brood inside the nest

14. Why do animals swarm? To forage better To migrate As a defense against predators Social Insects have survived for millions of years.

15. Swarm Intelligence: Applying • “Swarm” refers to a large group of simple components working together to achieve a goal and produce significant results. • Swarm intelligence techniques are population-based stochastic methods used in combinatorial optimization problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. • Swarm intelligence has become a hot research topic in both biology and engineering, exhibiting some important features such as decentralization, flexibility, robustness, self-organization and emergence

16. Swarm Intelligence: Applying • Intelligent swarm technology is based on aggregates of individual swarm members that also exhibit independent intelligence. • Members of the intelligent swarm can be heterogeneous or homogeneous. • Due to their differing environments, members can become a heterogeneous swarm as they learn different tasks and develop different goals, even if they begin as homogeneous.

17. Swarm Intelligence: Applying • The individuals in a group are called agents. • Swarm Intelligence based techniques can be applied to solve complicated problems • Data analysis problems, e.g., data clustering, to group the data with some features into the same clusters by using similarity measures • An ant with random motion picks up or drops data with a probability. • Further to cluster the data, measuring the similarity and dissimilarity between data are utilised. • This clustering algorithm is very efficient for few data sources, but not applicable to numerous data.

18. Swarm Intelligence: Applying • Network Routing Problems: utilize mobile software agents for network management • Agents are autonomous entities, both proactive and reactive, and have the capability to adapt, cooperate and move intelligently from one location to the other in the communication network • Swarm intelligence, uses stigmergy for agent interaction • Swarm intelligence exhibits emergent behavior wherein simple interactions of autonomous agents, with simple primitives, give rise to a complex behavior that has not been specified explicitly

19. Swarm Intelligence: Applying • Swarm Robotics • Swarm robotics refers to the application of swarm intelligence techniques to the analysis of activities in which the agents are physical robotic devices that can effect changes in their environments based on intelligent decision-making from various input. • The robots can walk, move on wheels, or operate under water or on other planets. • Job Scheduling Problems • Investigating the flexible way in which honeybees assign tasks could lead to a more efficient method for scheduling jobs in a factory

20. Existing Algorithms and Applications • Existing Algorithms • Particle swarm optimization (PSO) • Ant colony optimization (ACO) • Existing Applications • Unmanned underwater vehicles (UUV) • Swarmcasting Slocum Glider. Credit: Teledyne Webb Research via Ocean Observatories Initiative

21. Introduction to Particle swarm optimization (PSO) • PSO simulates the behaviors of bird flocking. • A group of birds are randomly searching food in an area. • There is only one piece of food in the area being searched. • All the birds do not know where the food is. • But they know how far the food is in each iteration. • What's the best strategy to find the food? • The effective one is to follow the bird which is nearest to the food.

22. Introduction to Particle swarm optimization (PSO) • In PSO, each single solution is a "bird" in the search space, called "particle". • All of particles have fitness values which are evaluated by the fitness function to be optimized, and have velocities which direct the flying of the particles. • The particles fly through the problem space by following the current optimum particles.

23. Introduction to Particle swarm optimization (PSO) • PSO is a global optimization algorithm for dealing with problems in which a point or surface in an n dimensional space best represents a solution. • Potential solutions are plotted in this space and seeded with an initial velocity. • Particles move through the solution space, and certain fitness criteria evaluate them. • Over time, particles accelerate toward those with better fitness values.

24. Introduction to Ant colony optimization (ACO) • Artificial ants travel through a problem graph depositing artificial or digital pheromones to enable other ants to determine more optimal solutions. • Ant colony optimization has solved the traveling salesman problem, which investigates the shortest route to several cities and the subsequent return to a starting point, as well as network and Internet optimizations.

25. Introduction to Ant colony optimization (ACO) Ant Colony OptimizationAlgorithms http://www.funpecrp.com.br/gmr/year2005/vol3-4/wob09_full_text.htm 25

26. Introduction to Unmanned underwater vehicles (UUV) • Each UUV relies on the same template information containing plans, subplans, and its own local situation map to make independent decisions. • The UUVs cooperate in the network • for example, group pursuit strategy experiments in a shallow water pool. • They can identify vessels of interest and pursue them in environments in which a larger underwater vessel would be destroyed.

27. Introduction to Swarmcast • A technique that exploits the acceleration of distributed downloading to provide high-resolution video, audio, and peer-to-peer data streams, swarmcasting also significantly reduces needed bandwidth. • It applies the swarm analogy to break down video files into small pieces so that the system can download components from several machines simultaneously. • The user can start watching the video before the download completes. • Swarmcast (www.swarmcast.com), a commercial company, supports delivery of large amounts of data over networks using similar concepts and strives to be a significant contributor to the next generation of Internet TV.

28. PSO: Details • PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. • In every iteration, each particle is updated by following two "best" values. • The first one is the best solution (fitness) it has achieved so far. (The fitness value is also stored.) • This value is called pbest. • Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the population. • This best value is a global best and called gbest. • When a particle takes part of the population as its topological neighbors, the best value is a local best and is called lbest.

29. PSO: Details • After finding the two best values, the particle updates its velocity and positions with following equations Velocity v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] - present[]) Position present[] = persent[] + v[] (b) v[] is the particle velocity, persent[] is the current particle (solution). pbest[] and gbest[] are defined as stated before. rand () is a random number between (0,1). c1, c2 are learning factors, c1 = c2

30. PSO: Details • For each particle Initialize particleENDDoFor each particle Calculate fitness value If the fitness value is better than the best fitness value (pBest) in historyset current value as the new pBestEndChoose the particle with the best fitness value of all the particles as the gBest For each particle Calculate particle velocity according equation (Velocity)Update particle position according equation (Position)End While maximum iterations or minimum error criteria is not attained

31. PSO: Details Particle Swarm OptimizationAlgorithms (PSO) 31 http://www.sciencedirect.com/science/article/pii/S0960148109001232

32. Comparisons between Genetic Algorithm and PSO • Most of evolutionary techniques have the following procedure:1. Random generation of an initial population2. Calculate fitness value for each subject. It will directly depend on the distance to the optimum. 3. Reproduction of the population based on fitness values. 4. If requirements are met, then stop. Otherwise go back to 2. PSO shares many common points with GA. • Both algorithms start with a group of a randomly generated population • Both algorithms have fitness values to evaluate the population. • Both algorithms update the population and search for the optimium with random techniques. • Both systems do not guarantee success.

33. Comparisons between Genetic Algorithm and PSO • PSO does not have genetic operators like crossover and mutation. • Particles update themselves with the internal velocity. • They also have memory, which is important to the algorithm. • The information sharing mechanism in PSO is significantly differentfrom GAs. • In GAs, chromosomes share information with each other. • The whole population moves like a one group towards an optimal area. • In PSO, only gBest (or lBest) gives out the information to others. • It is a one -way information sharing mechanism. • The evolution only looks for the best solution. • All the particles tend to converge to the best solution quickly even in the local version in most cases.

34. ACO: Details Ant Colony OptimizationAlgorithms the Traveling Salesman Problem: An iterative algorithm At each iteration, a number of artificial ants are considered. Each of them builds a solution by walking from node to node on the graph with the constraint of not visiting any node that she has already visited in her walk. An ant selects the following node to be visited according to a stochastic mechanism that is biased by the pheromone: when in node i, the following node is selected stochastically among the previously unvisited ones if j has not been previously visited, it can be selected with a probability that is proportional to the pheromone associated with edge (i, j). the pheromone values are modified in order to bias ants in future iterations to construct solutions similar to the best ones previously constructed. 34

35. ACO: Details Ant Colony OptimizationAlgorithms 35

36. ACO: Details Ant Colony OptimizationAlgorithms ConstructAntSolutions: A set of m artificial ants constructs solutions from elements of a finite set of available solution components. ApplyLocalSearch: Once solutions have been constructed, and before updating the pheromone, it is common to improve the solutions obtained by the ants through a local search. UpdatePheromones: The aim of the pheromone update is to increase the pheromone values associated with good or promising solutions, and to decrease those that are associated with bad ones. Usually, this is achieved by decreasing all the pheromone values through pheromone evaporation by increasing the pheromone levels associated with a chosen set of good solutions. 36

37. Example: TSP Solving by ACO • Matlab files: Ant_Tsp (Example)

38. Swarm Intelligence Application of Swarm Principles: Swarm of Robotics http://www.youtube.com/watch?feature=player_embedded&v=rYIkgG1nX4E#! http://www.domesro.com/2008/08/swarm-robotics-for-domestic-use.html 38

39. References • P. Meesad, S. Sodsee, Z. Li and W.A. Halang, “A Distributed Data Clustering based on Multiple Colonies Swarm-like Agent,” Proc. Intl. Conf. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 618-621, 2009 . • Hinchey, M.G., Sterritt, R.,Rouff, C.,“Swarms and Swarm Intelligence”, Computer, Vol.40, Iss.4, pp.111-113, 2007. • Simon Garnier,Jacques Gautrais,Guy Theraulaz,“The biological principles of swarm intelligence,” Swarm Intelligence, Vol.1, Iss.1, pp.3-31, 2007. • Peter Miller, “Smart Swarm: Using Animal Behaviour to Organise Our World,” Collins, 2011. • Swarm Intelligence, From Natural to Artificial Systems, Ukradnuté kde sa dalo, a adaptované. • http://www.swarmintelligence.org/ • http://www.isr.uci.edu/projects/pace/decentralization.html • Camazine, Deneubourg, Franks, Sneyd, Theraulaz, Bonabeau, “Self-Organization in Biological Systems,” Princeton University Press, 2003.