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PSO Variations

PSO Variations. Dr. Ashraf Abdelbar American University in Cairo. No Free Lunch Theorem.

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PSO Variations

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  1. PSO Variations Dr. AshrafAbdelbar American University in Cairo

  2. No Free Lunch Theorem • In a controversial paper in 1997 (available at AUC library), Wolpert and Macready proved that “averaged over all possible problems or cost functions, the performance of all search algorithms is exactly the same” • This includes such things as random search • No algorithm is better on average than blind guessing

  3. Cooperative PSO • The solution vector being optimized is divided into k parts, each part given to a separate sub-swarm. • Taken to the extreme, k can be equal n • To evaluate the fitness of each component in each subswarm, a context vector is used in which the component being evaluated is inserted. • One approach to forming a context vector is to take the currently global best component from each sub-swarm.

  4. Guaranteed-Convergence PSO

  5. Using PSO to train a game player

  6. Hierarchical PSO

  7. Hierarchical PSO

  8. Fully-Informed PSO

  9. Fully-Informed PSO Variants

  10. Clerc’s Type 1” Constriction

  11. Adaptive swarm size I try to kill myself There has been enough improvement although I'm the worst I try to generate a new particle I'm the best but there has been not enough improvement Maurice.Clerc@WriteMe.com This slide is taken from a presentation by M. Clerc

  12. Cluster Centers • c-means algorithm used to cluster x vectors • Cluster center vectors used instead of either the personal-best vectors or the neighborhood-best vectors

  13. Angeline’s Adaptation • In each iteration, the worst half of the population was replaced by mutated clones of the better half.

  14. Adaptive Noise

  15. Breeding Swarms

  16. Statistical Significance • When comparing two or more different techniques or variations or parameter settings on a given problem, it is important to make more than one run • You should at least report the mean and standard deviation (2σ includes 95%) • Ideally, you should run a test of statistical significance such as ANOVA • These tests are standard in the natural sciences, but sadly they are less common in CS

  17. Topics • Binary PSO • Continuous PSO • NFL • Inertia • Constriction • Adapting Swarm Size • Cluster centers • Angeline’s adaptation • Guaranteed Converge • Cooperative • Game playing • Hierarchical • Fully informed • Statistical Significance

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