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Unexpected stagnation with VonNeumann and Pyramid topologies #521
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I am adding more info with the following error that occurred after waiting on a stagnated pyramid pso. 2023-10-26 07:42:52,778 - pyswarms.single.general_optimizer - INFO - Optimize for 500 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.5, 'r': 15, 'p': 1} While executing: | qhull d Qt QJ0.001 Qc Qbb Qx At error exit: Delaunay triangulation by the convex hull of 50 points in 26-d: Number of input sites: 39 Statistics for: | qhull d Qt QJ0.001 Qc Qbb Qx Number of points processed: 38 During handling of the above exception, another exception occurred: Traceback (most recent call last): |
Dear pyswarms contributors and maintainers,
I have noticed while extensively using pyswarms for neural network training when i am using as topology the VonNeumann or Pyramid architectures, my swarms are facing a strong stagnation that i cannot explain. The error i think it has to do with the argument r which is the range of VonNeumann topology (which i can't find it anywhere in the literature except some references that are not exactly describing what you mean by r). Some of the times, due to r selection in combination with the number of particles returns the following error
Traceback (most recent call last):
File "/home/earthtech/salinity/von_neumann_tuning_8.py", line 131, in
cost, pos = optimizer.optimize(f, iters=iters)
File "/home/earthtech/.local/lib/python3.10/site-packages/pyswarms/single/general_optimizer.py", line 252, in optimize
self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
File "/home/earthtech/.local/lib/python3.10/site-packages/pyswarms/backend/topology/von_neumann.py", line 50, in compute_gbest
return super(VonNeumann, self).compute_gbest(swarm, p, k)
File "/home/earthtech/.local/lib/python3.10/site-packages/pyswarms/backend/topology/ring.py", line 78, in compute_gbest
idx_min = swarm.pbest_cost[self.neighbor_idx].argmin(axis=1)
IndexError: index 64 is out of bounds for axis 0 with size 64
My neural network is for regression analysis with 1 hidden layer of 6 neurons, 8 inputs at the input layer and 1 neuron in the output.
I have executed a lot of tests, hyperparameter tuning procedures and validation procedures with the same source code on a large number of feature subsets with LocalBest and GlobalBest PSO implementations and i faced no problem.
I am running on Ubuntu 20.04, python 3.10 and pyswarms 1.3.0
I have tried to contact some of you through email or personal accounts on social media.
Hope that we will find a solution.
Thanks for your contribution to the Evolutionary Intelligence community.
Best Regards,
Earthtech
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