A Study on Time-varying Partially Connected
Topologies for the Particle Swarm

Fernandes, Merelo, Laredo, Cotta, RosaUGR+UMA+UNILU+IST

Imagen de fondo de Simon Strandgaard en http://www.flickr.com/photos/12739382@N04/2411501455/

Particle Swarm optimization

This slide is taken from http://www.slideshare.net/many87/pso-apiems2009ppt and reproduced using the free license of that presentation.

No formula here, get a move on

Movement combines inertia term with following the best.

The best is a term that depends on population topology.You only follow the best if you know about it.

Different population structures are defined.

Act local, think global

LBest

GBest

Problem with gbest is that it usually get stuck, but it's very fast, lbest is slower, but it is usually able to find the solution.

A bit of this and that

Von Neumann

A dynamic, partially connected, population structure

Nodes move every timestep to one of the nodes in their Moore Neighborhood, if there's one available.

Testing the new populaton structure

Using the usual Sphere, Rosenbrock, Rastrigin, Griewank and Schaffer functions.

Best fitness at the origin.

Multimodal.

Usually difficult for gbest

Comparison with static vN: best fitness

Comparison with static vN: iterations

Comparison with l/gbest

Comparison with l/gbest: iterations

Iterations to solution in successful runs

VN faster than lbest, some times also faster than gbest

Rank by success rate

Rank by overall performance

Connectivity histogram.

Evolution of connectivity

Conclusions

Dynamic and partially connected structures offer the best of both worlds.

Size of the grid is not critical, but as a rule of thumb: 2 x number of particles.

Dynamic structures are better than static.

That's all

Any questions?

Check us out at @geneura@canubeproject

ANYSELF

AnyselfProject

@AnyselfProject

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8x80.5545.38215.37414.7413.949

9x92.47111.54116.4258.2591.304

10x105.36315.45214.0784.6320.475

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1000NaNNaNNaNNaNNaN

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