| Algorithm 1 Harmonic search algorithm pseudo-code . |
|---|
| 1: Define fitness value function fitness(t) = f(x), x = (x1, x2, x3, …, xn).n |
| 2: Define the generation range of harmony and the generating function of harmony. |
| 3: Set the algorithm parameters: harmony library size (HMS), harmony library value probability (HMCR), pitch adjustment probability (PAR), step size factor (BW), and maximum creation times (MAXGEN). |
| 4: Initialize the harmony library. |
| 5: Evaluate the fitness value of the sound library. |
| 6: Take the harmony best with the largest fitness value in the harmony library and set t = 0. |
| 7: whilet < MAXGEN − 1 do |
| 8: if random.random() < HMCR then |
| 9: if random.random() < PAR then |
| 10: Take a random harmony inv from the harmony library |
| 11: aa = np.random.randint(0,self.len,size = BW) |
| 12: fori = 0 → BW − 1 do |
| 13: Fine-tuning the variables corresponding to the harmony to obtain a new harmony invx. |
| 14: end for |
| 15: end if |
| 16: else |
| 17: Generate new harmony invx |
| 18: end if |
| 19: The new harmony invx is compared with the harmony invy with the worst solution in the harmony library, and updated if it is better than it. |
| 20: Update the harmony best with the maximum fitness value in the harmony library again. |
| 21: t = t + 1 |
| 22: end while |
| Algorithm 1 Harmonic search algorithm pseudo-code . |
|---|
| 1: Define fitness value function fitness(t) = f(x), x = (x1, x2, x3, …, xn).n |
| 2: Define the generation range of harmony and the generating function of harmony. |
| 3: Set the algorithm parameters: harmony library size (HMS), harmony library value probability (HMCR), pitch adjustment probability (PAR), step size factor (BW), and maximum creation times (MAXGEN). |
| 4: Initialize the harmony library. |
| 5: Evaluate the fitness value of the sound library. |
| 6: Take the harmony best with the largest fitness value in the harmony library and set t = 0. |
| 7: whilet < MAXGEN − 1 do |
| 8: if random.random() < HMCR then |
| 9: if random.random() < PAR then |
| 10: Take a random harmony inv from the harmony library |
| 11: aa = np.random.randint(0,self.len,size = BW) |
| 12: fori = 0 → BW − 1 do |
| 13: Fine-tuning the variables corresponding to the harmony to obtain a new harmony invx. |
| 14: end for |
| 15: end if |
| 16: else |
| 17: Generate new harmony invx |
| 18: end if |
| 19: The new harmony invx is compared with the harmony invy with the worst solution in the harmony library, and updated if it is better than it. |
| 20: Update the harmony best with the maximum fitness value in the harmony library again. |
| 21: t = t + 1 |
| 22: end while |
| Algorithm 1 Harmonic search algorithm pseudo-code . |
|---|
| 1: Define fitness value function fitness(t) = f(x), x = (x1, x2, x3, …, xn).n |
| 2: Define the generation range of harmony and the generating function of harmony. |
| 3: Set the algorithm parameters: harmony library size (HMS), harmony library value probability (HMCR), pitch adjustment probability (PAR), step size factor (BW), and maximum creation times (MAXGEN). |
| 4: Initialize the harmony library. |
| 5: Evaluate the fitness value of the sound library. |
| 6: Take the harmony best with the largest fitness value in the harmony library and set t = 0. |
| 7: whilet < MAXGEN − 1 do |
| 8: if random.random() < HMCR then |
| 9: if random.random() < PAR then |
| 10: Take a random harmony inv from the harmony library |
| 11: aa = np.random.randint(0,self.len,size = BW) |
| 12: fori = 0 → BW − 1 do |
| 13: Fine-tuning the variables corresponding to the harmony to obtain a new harmony invx. |
| 14: end for |
| 15: end if |
| 16: else |
| 17: Generate new harmony invx |
| 18: end if |
| 19: The new harmony invx is compared with the harmony invy with the worst solution in the harmony library, and updated if it is better than it. |
| 20: Update the harmony best with the maximum fitness value in the harmony library again. |
| 21: t = t + 1 |
| 22: end while |
| Algorithm 1 Harmonic search algorithm pseudo-code . |
|---|
| 1: Define fitness value function fitness(t) = f(x), x = (x1, x2, x3, …, xn).n |
| 2: Define the generation range of harmony and the generating function of harmony. |
| 3: Set the algorithm parameters: harmony library size (HMS), harmony library value probability (HMCR), pitch adjustment probability (PAR), step size factor (BW), and maximum creation times (MAXGEN). |
| 4: Initialize the harmony library. |
| 5: Evaluate the fitness value of the sound library. |
| 6: Take the harmony best with the largest fitness value in the harmony library and set t = 0. |
| 7: whilet < MAXGEN − 1 do |
| 8: if random.random() < HMCR then |
| 9: if random.random() < PAR then |
| 10: Take a random harmony inv from the harmony library |
| 11: aa = np.random.randint(0,self.len,size = BW) |
| 12: fori = 0 → BW − 1 do |
| 13: Fine-tuning the variables corresponding to the harmony to obtain a new harmony invx. |
| 14: end for |
| 15: end if |
| 16: else |
| 17: Generate new harmony invx |
| 18: end if |
| 19: The new harmony invx is compared with the harmony invy with the worst solution in the harmony library, and updated if it is better than it. |
| 20: Update the harmony best with the maximum fitness value in the harmony library again. |
| 21: t = t + 1 |
| 22: end while |
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