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What we started with

What we started with. To develop an application that facilitates user to draw interactive graphs (e.g. Flowcharts ,class diagrams, UML diagrams etc ).

quail-warner
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What we started with

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  1. What we started with • To develop an application that facilitates user to draw interactive graphs (e.g. Flowcharts ,class diagrams, UML diagrams etc ). • Interactive graphs arrange Nodes themselves in the "canvas" dynamically as the user adds diagrams ( calculates the best positions for different nodes, resizes them, and fits them canvas in such a way that it looks aesthetically pleasing to the user. • It should also arrange according to the groups.( Grouping similar Nodes).

  2. Various techniques analyzed to tackle this problem statement. • Fundamental forces (Force Based Algorithms) • Result from exchange of “carrier particles” • Equilibrium via combination of attraction & repulsion • Diffusion of gaseous matter(Simulated Annealing) • Particles tend toward a uniform distribution • Multi-agent swarms & crowd motion (Genetic & PSO) • Global motion emerges from local behavior • “Thinking fluids” exhibit unique other properties

  3. Meta-Heuristics • A name for any stochastic optimization algorithm intended to be the last resort before giving up and using random or brute-force search. • Such algorithms are used for problems where you don't know how to find a good solution, but if shown a candidate solution, you can give it a grade. • The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on.

  4. Meta-Heuristics Figure 0 The Mona Lisa, estimated with the (5 + 1) Evolution Strategy. The objective was to find a set of fifty polygons which most closely approximated the original image. Reference: Essentials of Metaheuristics

  5. Why Meta-Heuristics? • We don’t have a definite solution. • Our objective functions are dynamically changing. • Our needs multi-objective functions to achieve the desired goal. • Third approach gave more opportunity for research and solution for variety of problems. • Existing successful applications. • Brief explanation of how this approach can solve our problem statement.

  6. Our Motivation-Social Behaviour • 1.For Communication-SwarmmingBehaviour of Bees. • A video or an animation.

  7. Our Motivation-Social Behaviour • For Layout- Flocking of Bird • “Basic models of flocking behavior are controlled by three simple rules: • Separation - avoid crowding neighbors (short range repulsion) • Alignment - steer towards average heading of neighbors • Cohesion - steer towards average position of neighbors (long range attraction) • With these three simple rules, the flock moves in an extremely realistic way, creating complex motion and interaction that would be extremely hard to create otherwise.”” Wiki • A video showing how flocks maintain above parameters.

  8. Basic PSO Algorithm Flowchart of this. • 1. Create a ‘population’ of agents (called particles) uniformly distributed over X. • 2. Evaluate each particle’s position according to the objective function. • 3. If a particle’s current position is better than its previous best position, update it. • 4. Determine the best particle (according to the particle’s previous • best positions). • 5. Update particles’ velocities according to equation: • 6. Move particles to their new positions according to equation: • Go to step 2 until stopping criteria are satisfied. • Use the term Social Influence. Fully informed PSO.

  9. Our approach • Variations of PSO. • Our approach.(PAPSO). • The sequential synchronous PSO algorithm updates all particle velocities and positions at the • end of every optimization iteration (Figure 1). In contrast, the sequential asynchronous PSO • algorithm updates particle positions and velocities continuously based on currently available • Less costly computationally to achieve convergence. • Fig of page 5. • Since PAPSO incorporates a dynamic load balancing, scheme, parallel performance is dramatically increased for (1) heterogeneous computing environments,(2) user-loaded computing environments and (3) problems producing run-time load. • was 3.5 times faster than was PSPSO for the biomechanical test problem executed on a heterogeneous • cluster with 20 processors. Overall, PAPSO exhibits excellent parallel performance when a • large number of processors (more than about 15) is utilized and either (1) heterogeneity exists in the • computational task or environment, or (2) the computation-to-communication time ratio is relatively • small.

  10. Our first attempt(Algorithm) • Design of various classes.

  11. Class Diagram

  12. Model-View/Observer Design Pattern

  13. Factory, Iterator, Thread, Singleton, Façade Pattern use at different places. • Every swimmable runs in Thread. • All swimmable created from same base class. etc.

  14. Swimmable Behavior

  15. Testing and Observations • 1.Demo. • 2.Problems seen which hindered to achieve our goal. • Particles got stuck due to memory of previous best. • Tried to Converge at a single point. • Single Test case not successful.(Particle bumped off the screen after being equidistant from its neighbors) • Trying to fit both objectives at the same was time not successful.

  16. 2nd Attempt. • Meta-swarm using existing architecture. • Obstacles(1-2)

  17. Latest Attempt. • Eliminate manager.

  18. TODOs • 1.More adaptive tuning. • Dynamic sleep interval • Dynamic inertia/learning • Global objective(meta-swarm) • Performance Analysis. • Not working- • Missing-Global Objective(meta-swarm)-Meta swarm class.

  19. Futurework • Real truly distributed.(RMI) • Multi-Objective • Other applications for designs.

  20. Application Domain of PSO/Swarm Intelligence • Crowd simulation- develop crowd controlling strategies. Emergency response teams such as policemen, the National Guard, military • Telecommunication Networks ,Network (Ant-based routing)so as to achieve minimum loss of information without degrading robustness , performance and efficiency.(Fault tolerating) • It influencs Emergence behaviour Emergent processes or behaviours can be seen in many places, such as traffic patterns, cities, political systems of governance, cabal and market-dominant minority phenomena in politics and economics(Stock-market),WWW and Internet. • drug trafficking exhibit similar self-organizing properties. Parallel examples exist in the world of privacy-preserving computer networks such as Tor. In each case, the network as a whole exhibits distinctive synergistic behavior through the combination of the behaviors of individual actors in the network. Usually the growth of such networks is fueled by an ideology or sociological force that is adhered to or shared by all participants in the network

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