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I got the error as "IndexError: invalid index to scalar variable.", while trying this PSO algorithm problem. I also want to plot a graph between iteration number and fitness value. Kindly help me with this.

import random
import numpy as np 
import math
import matplotlib.pyplot as plt

def fitness_function(position):
    s1=0.014109786*position[0] + 0.004596846*position[1] + 0.01603721*position[2] + 0.029275618*position[3] + 0.007085358*position[4] + 0.013234328*position[5] + 0.012554958*position[6] + 0.012447232*position[7] + 0.007867602*position[8] + 0.011312568*position[9] + 0.003087858*position[10] + 0.016566954*position[11] + 0.008428942*position[12] + 0.008477444*position[13] + 0.004357354*position[14]  
    s2=0.016566954*position[0] + 0.00585045*position[1] + 0.053172638*position[2] + 0.113404042*position[3] + 0.028190744*position[4] + 0.046330688*position[5] + 0.05629084*position[6] + 0.047796486*position[7] + 0.025793022*position[8] + 0.046164518*position[9] + 0.026696192*position[10] + 0.080422654*position[11] + 0.029074508*position[12] + 0.039611624*position[13] + 0.044835566*position[14] 
    return s1+s2

#Some variables to calculate the velocity
W = 0.5
c1 = 0.8
c2 = 0.9
target = 1

n_iterations = int(input("Inform the number of iterations: "))
target_error = float(input("Inform the target error: "))
n_particles = int(input("Inform the number of particles: "))

particle_position_vector=np.array([np.random.uniform(low=0.025, high=0.035) for i in range (n_particles)])
print(particle_position_vector)
pbest_position = particle_position_vector
pbest_fitness_value = float('inf')
gbest_fitness_value = float('inf')
gbest_position = np.array([float('inf')for _ in range(n_particles)])

velocity_vector =np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0])
print(velocity_vector)

iteration = 0
while iteration < n_iterations:
    for i in range(n_particles):
        fitness_cadidate = fitness_function(particle_position_vector[i])
        print(fitness_cadidate, ' ', particle_position_vector[i])
        
        if(pbest_fitness_value[i] > fitness_cadidate):
            pbest_fitness_value[i] = fitness_cadidate
            pbest_position[i] = particle_position_vector[i]

        if(gbest_fitness_value > fitness_cadidate):
            gbest_fitness_value = fitness_cadidate
            gbest_position = particle_position_vector[i]

    if(abs(gbest_fitness_value - target) < target_error):
        break
    
    for i in range(n_particles):
        new_velocity = (W*velocity_vector[i]) + (c1*random.random()) * (pbest_position[i] - particle_position_vector[i]) + (c2*random.random()) * (gbest_position-particle_position_vector[i])
        new_position = new_velocity + particle_position_vector[i]
        particle_position_vector[i] = new_position

    iteration = iteration + 1
    
print("The best position is ", gbest_position, "in iteration number ", iteration)

Here, I have posted the code with an error. Please help me.

import random
import numpy as np 
import math
import matplotlib.pyplot as plt

def fitness_function(position):
    s1=0.014109786*position[0] + 0.004596846*position[1] + 0.01603721*position[2] + 0.029275618*position[3] + 0.007085358*position[4] + 0.013234328*position[5] + 0.012554958*position[6] + 0.012447232*position[7] + 0.007867602*position[8] + 0.011312568*position[9] + 0.003087858*position[10] + 0.016566954*position[11] + 0.008428942*position[12] + 0.008477444*position[13] + 0.004357354*position[14]  
    s2=0.016566954*position[0] + 0.00585045*position[1] + 0.053172638*position[2] + 0.113404042*position[3] + 0.028190744*position[4] + 0.046330688*position[5] + 0.05629084*position[6] + 0.047796486*position[7] + 0.025793022*position[8] + 0.046164518*position[9] + 0.026696192*position[10] + 0.080422654*position[11] + 0.029074508*position[12] + 0.039611624*position[13] + 0.044835566*position[14] 
    return s1+s2

#Some variables to calculate the velocity
W = 0.5
c1 = 0.8
c2 = 0.9
target = 1

n_iterations = int(input("Inform the number of iterations: "))
target_error = float(input("Inform the target error: "))
n_particles = int(input("Inform the number of particles: "))

particle_position_vector=np.array([np.random.uniform(low=0.025, high=0.035) for i in range (n_particles)])
print(particle_position_vector)
pbest_position = particle_position_vector
pbest_fitness_value = float('inf')
gbest_fitness_value = float('inf')
gbest_position = np.array([float('inf')for _ in range(n_particles)])

velocity_vector =np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0])
print(velocity_vector)

iteration = 0
while iteration < n_iterations:
    for i in range(n_particles):
        fitness_cadidate = fitness_function(particle_position_vector[i])
        print(fitness_cadidate, ' ', particle_position_vector[i])
        
        if(pbest_fitness_value[i] > fitness_cadidate):
            pbest_fitness_value[i] = fitness_cadidate
            pbest_position[i] = particle_position_vector[i]

        if(gbest_fitness_value > fitness_cadidate):
            gbest_fitness_value = fitness_cadidate
            gbest_position = particle_position_vector[i]

    if(abs(gbest_fitness_value - target) < target_error):
        break
    
    for i in range(n_particles):
        new_velocity = (W*velocity_vector[i]) + (c1*random.random()) * (pbest_position[i] - particle_position_vector[i]) + (c2*random.random()) * (gbest_position-particle_position_vector[i])
        new_position = new_velocity + particle_position_vector[i]
        particle_position_vector[i] = new_position

    iteration = iteration + 1
    
print("The best position is ", gbest_position, "in iteration number ", iteration)

Error:
IndexError                                Traceback (most recent call last)
<ipython-input-16-5610603d3302> in <module>
     32 while iteration < n_iterations:
     33     for i in range(n_particles):
---> 34         fitness_cadidate = fitness_function(particle_position_vector[i])
     35         print(fitness_cadidate, ' ', particle_position_vector[i])
     36 

<ipython-input-16-5610603d3302> in fitness_function(position)
      5 
      6 def fitness_function(position):
----> 7     s1=0.014109786*position[0] + 0.004596846*position[1] + 0.01603721*position[2] + 0.029275618*position[3] + 0.007085358*position[4] + 0.013234328*position[5] + 0.012554958*position[6] + 0.012447232*position[7] + 0.007867602*position[8] + 0.011312568*position[9] + 0.003087858*position[10] + 0.016566954*position[11] + 0.008428942*position[12] + 0.008477444*position[13] + 0.004357354*position[14]
      8     s2=0.016566954*position[0] + 0.00585045*position[1] + 0.053172638*position[2] + 0.113404042*position[3] + 0.028190744*position[4] + 0.046330688*position[5] + 0.05629084*position[6] + 0.047796486*position[7] + 0.025793022*position[8] + 0.046164518*position[9] + 0.026696192*position[10] + 0.080422654*position[11] + 0.029074508*position[12] + 0.039611624*position[13] + 0.044835566*position[14]
      9     return s1+s2

IndexError: invalid index to scalar variable.

"fitness_function(position)" expects an array (or list or any iterable) of exactly 15 elements. When you do:

fitness_function(particle_position_vector[i])

... you are only passing one element. So I guess you have to pass the whole array, like this:

fitness_function(particle_position_vector)

Note that there have to be 15 elements, so the only correct answer to:

input("Inform the number of particles: ")

... is 15.

(Mar-10-2022, 09:36 AM)ibreeden Wrote: [ -> ]"fitness_function(position)" expects an array (or list or any iterable) of exactly 15 elements. When you do:

fitness_function(particle_position_vector[i])

... you are only passing one element. So I guess you have to pass the whole array, like this:

fitness_function(particle_position_vector)

Note that there have to be 15 elements, so the only correct answer to:

input("Inform the number of particles: ")

... is 15.

import random
import numpy as np 
import math
import matplotlib.pyplot as plt

def fitness_function(position):
    s1=0.014109786*position[0] + 0.004596846*position[1] + 0.01603721*position[2] + 0.029275618*position[3] + 0.007085358*position[4] + 0.013234328*position[5] + 0.012554958*position[6] + 0.012447232*position[7] + 0.007867602*position[8] + 0.011312568*position[9] + 0.003087858*position[10] + 0.016566954*position[11] + 0.008428942*position[12] + 0.008477444*position[13] + 0.004357354*position[14]  
    s2=0.016566954*position[0] + 0.00585045*position[1] + 0.053172638*position[2] + 0.113404042*position[3] + 0.028190744*position[4] + 0.046330688*position[5] + 0.05629084*position[6] + 0.047796486*position[7] + 0.025793022*position[8] + 0.046164518*position[9] + 0.026696192*position[10] + 0.080422654*position[11] + 0.029074508*position[12] + 0.039611624*position[13] + 0.044835566*position[14] 
    return s1+s2

#Some variables to calculate the velocity
W = 0.5
c1 = 0.8
c2 = 0.9
target = 1

n_iterations = int(input("Inform the number of iterations: "))
target_error = float(input("Inform the target error: "))
n_particles = int(input("Inform the number of particles: "))

particle_position_vector=np.array([np.random.uniform(low=0.025, high=0.035) for i in range (n_particles)])
print(particle_position_vector)
pbest_position = particle_position_vector
pbest_fitness_value = float('inf')
gbest_fitness_value = float('inf')
gbest_position = np.array([float('inf')for _ in range(n_particles)])

velocity_vector =np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])
print(velocity_vector)

iteration = 0
while iteration < n_iterations:
    for i in range(n_particles):
        fitness_cadidate = fitness_function(particle_position_vector)
        print([fitness_cadidate, ' ', particle_position_vector[i]])
        
        if(pbest_fitness_value > fitness_cadidate):
            pbest_fitness_value = fitness_cadidate
            pbest_position[i] = particle_position_vector[i]

        if(gbest_fitness_value > fitness_cadidate):
            gbest_fitness_value = fitness_cadidate
            gbest_position = particle_position_vector[i]

    if(abs(gbest_fitness_value - target) < target_error):
        break
    
    for i in range(n_particles):
        new_velocity = (W*velocity_vector[i]) + (c1*random.random()) * (pbest_position[i] - particle_position_vector[i]) + (c2*random.random()) * (gbest_position-particle_position_vector[i])
        new_position = new_velocity + particle_position_vector[i]
        particle_position_vector[i] = new_position

    iteration = iteration + 1
    
print("The best position is ", gbest_position, "in iteration number ", iteration)

After modifying the code, I got the output like:

Output:
Inform the number of iterations: 10
Inform the target error: 0
Inform the number of particles: 15
[0.02731175 0.02615455 0.0316539  0.03217349 0.03422525 0.03060605
 0.0303541  0.03037021 0.02762708 0.03179528 0.03378483 0.02605968
 0.03378637 0.02818577 0.02678802]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0.025073619876763563, ' ', 0.027311746421561187]
[0.025073619876763563, ' ', 0.026154548512811418]
[0.025073619876763563, ' ', 0.031653900892799076]
[0.025073619876763563, ' ', 0.03217348852973725]
[0.025073619876763563, ' ', 0.03422524835447013]
[0.025073619876763563, ' ', 0.030606050682032628]
[0.025073619876763563, ' ', 0.030354099198015336]
[0.025073619876763563, ' ', 0.03037021067585577]
[0.025073619876763563, ' ', 0.02762708294702282]
[0.025073619876763563, ' ', 0.0317952794785016]
[0.025073619876763563, ' ', 0.03378483403043713]
[0.025073619876763563, ' ', 0.026059682094216857]
[0.025073619876763563, ' ', 0.03378636800677858]
[0.025073619876763563, ' ', 0.02818576945560601]
[0.025073619876763563, ' ', 0.02678802479348601]
[0.024236544801469147, ' ', 0.027311746421561187]
[0.024236544801469147, ' ', 0.026415891253887314]
[0.024236544801469147, ' ', 0.028604008990511874]
[0.024236544801469147, ' ', 0.03074774274512092]
[0.024236544801469147, ' ', 0.03414078591895475]
[0.024236544801469147, ' ', 0.030306985044460125]
[0.024236544801469147, ' ', 0.030119641891818583]
[0.024236544801469147, ' ', 0.028793472393863595]
[0.024236544801469147, ' ', 0.027449975183880045]
[0.024236544801469147, ' ', 0.03111075257312244]
[0.024236544801469147, ' ', 0.030622561175236245]
[0.024236544801469147, ' ', 0.02672282825332497]
[0.024236544801469147, ' ', 0.028461712812135026]
[0.024236544801469147, ' ', 0.02777940411844139]
[0.024236544801469147, ' ', 0.026809028972117]
[0.02346529762638839, ' ', 0.027311746421561187]
[0.02346529762638839, ' ', 0.026981469671633156]
[0.02346529762638839, ' ', 0.02813767141334174]
[0.02346529762638839, ' ', 0.029592069925483532]
[0.02346529762638839, ' ', 0.02837790911491178]
[0.02346529762638839, ' ', 0.028364146371212394]
[0.02346529762638839, ' ', 0.028994207307403735]
[0.02346529762638839, ' ', 0.028521130354018574]
[0.02346529762638839, ' ', 0.02733382284896558]
[0.02346529762638839, ' ', 0.02857768157227375]
[0.02346529762638839, ' ', 0.02989461808249676]
[0.02346529762638839, ' ', 0.026756018496818988]
[0.02346529762638839, ' ', 0.028287748253333814]
[0.02346529762638839, ' ', 0.02757727416516129]
[0.02346529762638839, ' ', 0.02715715630639833]
[0.023079901618592028, ' ', 0.027311746421561187]
[0.023079901618592028, ' ', 0.027119083111800918]
[0.023079901618592028, ' ', 0.027535232430653558]
[0.023079901618592028, ' ', 0.029197730613825836]
[0.023079901618592028, ' ', 0.028117580842869583]
[0.023079901618592028, ' ', 0.027493561827534218]
[0.023079901618592028, ' ', 0.027535533291056267]
[0.023079901618592028, ' ', 0.02757013872393907]
[0.023079901618592028, ' ', 0.027327942535210065]
[0.023079901618592028, ' ', 0.02767694012644384]
[0.023079901618592028, ' ', 0.029576419946264126]
[0.023079901618592028, ' ', 0.0269177244497189]
[0.023079901618592028, ' ', 0.027621399707694026]
[0.023079901618592028, ' ', 0.027497623272480922]
[0.023079901618592028, ' ', 0.02724789858712283]
[0.022857406338736855, ' ', 0.027311746421561187]
[0.022857406338736855, ' ', 0.02727615412256947]
[0.022857406338736855, ' ', 0.027532918917815962]
[0.022857406338736855, ' ', 0.027888958853220198]
[0.022857406338736855, ' ', 0.027753784828660758]
[0.022857406338736855, ' ', 0.027483166222550988]
[0.022857406338736855, ' ', 0.027368350958994935]
[0.022857406338736855, ' ', 0.027543632073428474]
[0.022857406338736855, ' ', 0.027324741812849527]
[0.022857406338736855, ' ', 0.027405121812830897]
[0.022857406338736855, ' ', 0.029140860085802158]
[0.022857406338736855, ' ', 0.027148195061810553]
[0.022857406338736855, ' ', 0.027540830701325278]
[0.022857406338736855, ' ', 0.02746259693956002]
[0.022857406338736855, ' ', 0.027255515639827303]
[0.022791972609377237, ' ', 0.027311746421561187]
[0.022791972609377237, ' ', 0.027284161356972335]
[0.022791972609377237, ' ', 0.02752916345003667]
[0.022791972609377237, ' ', 0.02774892029312538]
[0.022791972609377237, ' ', 0.02754264634622429]
[0.022791972609377237, ' ', 0.02746954413807446]
[0.022791972609377237, ' ', 0.027326306883516054]
[0.022791972609377237, ' ', 0.027538228090200418]
[0.022791972609377237, ' ', 0.027323073432060686]
[0.022791972609377237, ' ', 0.027400338838344276]
[0.022791972609377237, ' ', 0.02803318847454111]
[0.022791972609377237, ' ', 0.027214939542158915]
[0.022791972609377237, ' ', 0.027339732821101005]
[0.022791972609377237, ' ', 0.027460151433373124]
[0.022791972609377237, ' ', 0.027269964969757813]
[0.022728270076583682, ' ', 0.027311746421561187]
[0.022728270076583682, ' ', 0.027299489741849824]
[0.022728270076583682, ' ', 0.02733994351843582]
[0.022728270076583682, ' ', 0.027503216790345134]
[0.022728270076583682, ' ', 0.02748338015769847]
[0.022728270076583682, ' ', 0.02742669524665184]
[0.022728270076583682, ' ', 0.027313607986463772]
[0.022728270076583682, ' ', 0.027468219036881996]
[0.022728270076583682, ' ', 0.027320444909955617]
[0.022728270076583682, ' ', 0.02736316010077298]
[0.022728270076583682, ' ', 0.027688456643096663]
[0.022728270076583682, ' ', 0.027292324372632293]
[0.022728270076583682, ' ', 0.027337835510298385]
[0.022728270076583682, ' ', 0.027415978007609972]
[0.022728270076583682, ' ', 0.02729451630468392]
[0.022708601467485302, ' ', 0.027311746421561187]
[0.022708601467485302, ' ', 0.02730789193497009]
[0.022708601467485302, ' ', 0.027335580402316054]
[0.022708601467485302, ' ', 0.027498882543126997]
[0.022708601467485302, ' ', 0.027429306170710276]
[0.022708601467485302, ' ', 0.027324422214565162]
[0.022708601467485302, ' ', 0.02731317681215081]
[0.022708601467485302, ' ', 0.027461395509372235]
[0.022708601467485302, ' ', 0.027314259146785744]
[0.022708601467485302, ' ', 0.02733988779244826]
[0.022708601467485302, ' ', 0.02735648264439329]
[0.022708601467485302, ' ', 0.027299907896279316]
[0.022708601467485302, ' ', 0.027329487629375076]
[0.022708601467485302, ' ', 0.02741527015294955]
[0.022708601467485302, ' ', 0.027307711829914684]
[0.02267308392919699, ' ', 0.027311746421561187]
[0.02267308392919699, ' ', 0.027308091939796143]
[0.02267308392919699, ' ', 0.02732240619587771]
[0.02267308392919699, ' ', 0.027337748933687067]
[0.02267308392919699, ' ', 0.027330397694597778]
[0.02267308392919699, ' ', 0.02731869166187432]
[0.02267308392919699, ' ', 0.02731277323114734]
[0.02267308392919699, ' ', 0.02733616107745396]
[0.02267308392919699, ' ', 0.027313559924095284]
[0.02267308392919699, ' ', 0.02733288517115566]
[0.02267308392919699, ' ', 0.027337302826067517]
[0.02267308392919699, ' ', 0.027308300580075086]
[0.02267308392919699, ' ', 0.02732900163476884]
[0.02267308392919699, ' ', 0.027413729378012393]
[0.02267308392919699, ' ', 0.027308953090812722]
[0.022667700579661755, ' ', 0.027311746421561187]
[0.022667700579661755, ' ', 0.027309231186214207]
[0.022667700579661755, ' ', 0.027319489184052095]
[0.022667700579661755, ' ', 0.02733174822598569]
[0.022667700579661755, ' ', 0.027324770024226848]
[0.022667700579661755, ' ', 0.027316748243450862]
[0.022667700579661755, ' ', 0.027312610526234418]
[0.022667700579661755, ' ', 0.02731601220083983]
[0.022667700579661755, ' ', 0.027313197244750904]
[0.022667700579661755, ' ', 0.02731928310142291]
[0.022667700579661755, ' ', 0.027325061997480537]
[0.022667700579661755, ' ', 0.027308877051798842]
[0.022667700579661755, ' ', 0.02732898826003717]
[0.022667700579661755, ' ', 0.02737632007547092]
[0.022667700579661755, ' ', 0.02731107342103635]
The best position is  0.027311746421561187 in iteration number  10
​

But the problem is, it should be coming like (fitness value, [array of 15 particle's position]) for each iteration, which didn't come. Please help....