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Particle Swarm Optimization (PSO)

Particle Swarm Optimization (PSO). MATH 3220 Student Presentations By: Jennifer Lamb. Outline. Definition History How it works Scenario/Key words Pseudo-Code Algorithm Application Advantages/Disadvantages Works Cited. What is PSO?. How did it get its name?

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Particle Swarm Optimization (PSO)

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  1. Particle Swarm Optimization (PSO) MATH 3220 Student Presentations By: Jennifer Lamb

  2. Outline • Definition • History • How it works • Scenario/Key words • Pseudo-Code • Algorithm • Application • Advantages/Disadvantages • Works Cited

  3. What is PSO? • How did it get its name? • “A population based stochastic optimization technique” (Hu) • It provides a population-based search procedure • Getting the best solution from the problem by taking particles and moving them around in the search space

  4. HISTORY Dr. Eberhart and Dr. Kennedy 1995 Originated as a real life example of simplified social system. Intended to graphically simulate the choreography of bird of a bird block or fish school. Found that its model can be used as an optimizer.

  5. How does it work? • The system is initialized with a population of random solutions and searches for optima by updating generations. • There is no crossover and mutation. • The particles fly through the problem space by following the current optimum particles. (Hu)

  6. Scenario Strategy • Birds searching for food • Searching for one piece • Only know how far food is • Follow bird nearest to food Key Words • Particles • Fitness • Pbest • Values • Local best (lbest) • Global best (gbest) C:\Documents and Settings\User\Local Settings\Temp\alp_anim.gif

  7. Pseudo-Code For each particle     Initialize particleENDDo    For each particle         Calculate fitness value        If the fitness value is better than the best fitness value (pBest) in history            set current value as the new pBest    End    Choose the particle with the best fitness value of all the particles as the gBest    For each particle         Calculate particle velocity according equation (a)        Update particle position according equation (b)    End While maximum iterations or minimum error criteria is not attained

  8. Algorithm • v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] - present[]) • present[] = persent[] + v[] 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. usually c1 = c2 = 2.

  9. Application • Telecommunications • data mining • power systems • signal processing • Function optimization • artificial neural network training • fuzzy system control • where Genetic Algortihm can be applied.

  10. Advantages/Disadvantages • Easy to perform • Few parameters to adjust • Efficient in global search • Slow convergence in refined search stage • Weak local search ability

  11. Works Cited Hu, Xiaohui. "Particle Swarm Optimization." Particle Swarm Optimization. 10 Oct. 2010. <http://www.swarmintelligence.org/index.php>. "Particle Swarm Optimization." Scholarpedia. 15 Oct. 2010. <http://www.scholarpedia.org/article/Particle_swarm_optimization>. "Particle Swarm Optimization." Wikipedia, the Free Encyclopedia. 10 Oct. 2010. <http://en.wikipedia.org/wiki/Particle_swarm_optimization>. Schutte, Schutte F. "The Particle Swarm Optimization Algorithm.” 20 Oct. 2010. <www.mae.ufl.edu/haftka/stropt/Lectures/PSO_introduction.pdf>.

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