TypeError: 'bool' object is not subscriptable

[justinlent]

• PySwarms version: 0.19
• Python version: 3.6
• Operating System: Mac OS

Description

Describe what you were trying to get done.
Run a GlobalBestOptimization

Tell us what happened, what went wrong, and what you expected to happen.
I have a fairly complex objective function, but I can get it to run and return the expected values when running outside of pyswarms. but when I kick off an optimization i get this 'bool' object is not subsriptable TypeError after what appears to be the 1st iteration of the optimization (or maybe it's at the beginning of the 2nd iteration).

What I Did

# Create bounds
br = 0.2
max_bound = np.array( [ -1.0+br, 0.75,    0.5,    0.20,   0.9475,    0.9,    0.85,    0.8,    -1.5+br, 1.25,    6.0 ] )
min_bound = np.array( [ -1.0,    0.75-br, 0.5-br, 0.0-br, 0.9475-br, 0.9-br, 0.85-br, 0.8-br, -1.5,    1.25-br, 4.0 ] )

print( "Running strategy optimization with, # of parameters (p-swarm dimensions):", len(max_bound) )

bounds = (min_bound, max_bound)

# Initialize swarm
options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}

# Call instance of PSO with bounds argument
optimizer = ps.single.GlobalBestPSO(n_particles=2, dimensions=11, options=options, bounds=bounds)

# Perform optimization
cost, pos = optimizer.optimize(strat_obj_func_wrapper, print_step=5, iters=100, verbose=3)

Then running the optimizer results in the following output and traceback:

Running strategy optimization with, # of parameters (p-swarm dimensions): 11

 opt_run_inputs_multiple: [[-0.88239126  0.63317997  0.41620925 -0.06704406  0.93384025  0.77059523
   0.80944611  0.63739677 -1.43850201  1.10322874  5.4486611 ]
 [-0.90738229  0.57249487  0.30730725 -0.07771865  0.85251403  0.85871976
   0.68927081  0.62064886 -1.36711124  1.10884316  4.84986957]]
-0.07815433772752717
-0.00789521337447165

 opt_run_inputs_multiple: [[-0.88239126  0.63317997  0.41620925 -0.06704406  0.93384025  0.77059523
   0.80944611  0.63739677 -1.43850201  1.10322874  5.4486611 ]
 [-0.90738229  0.57249487  0.30730725 -0.07771865  0.85251403  0.85871976
   0.68927081  0.62064886 -1.36711124  1.10884316  4.84986957]]
-0.07815433772752717
-0.00789521337447165

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-547-2949e347e2b5> in <module>()
     15 
     16 # Perform optimization
---> 17 cost, pos = optimizer.optimize(strat_obj_func_wrapper, print_step=5, iters=100, verbose=3)

~/python/anaconda3510/anaconda3/envs/quant_py364/lib/python3.6/site-packages/pyswarms/single/global_best.py in optimize(self, objective_func, iters, print_step, verbose)
    151             pbest_cost = np.where(~m, pbest_cost, current_cost)
    152             # Create 2-D mask to update positions
--> 153             _m = np.repeat(m[:, np.newaxis], self.dimensions, axis=1)
    154             self.personal_best_pos = np.where(~_m, self.personal_best_pos,
    155                                               self.pos)

TypeError: 'bool' object is not subscriptable

[ljvmiranda921]

Hi @justinlent , sorry for the late reply.

I wonder what shape your custom objective function returns. As of now, custom functions should
always return a vector of shape (n_particles, ) where each element contains the fitness of each particle.

In order to interface properly with pyswarms, the objective function should:

1. Be able to take a matrix of shape (n_particles, n_dimensions)
2. Be able to return a vector of shape (n_particles, ) with the fitness for each particle.

Thanks and I hope it helps.

[justinlent]

Thanks! This appears to have fixed it for me!

[ljvmiranda921]

Glad it helped! Goodluck! 👍