Add file GJO.py #7
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| import numpy as np | ||
| from scipy.special import gamma | ||
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How to run this file? Where is the example to run the file?
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| """ | ||
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| def __init__(self, f_obj, dim = 2): |
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Where is the lower bound (LB) and upper bound (UB) parameters?
If you don't have LB and UB how can you modify the solution?
Remove the dim parameter, and then replace it with LB and UB.
| self.dim_ = dim | ||
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| def LF(self, beta): |
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name the function with full name, don't use short name. For example: levy_flight()
| return 0.01*(mu*sigm)/(v**(1/beta)) | ||
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| def model(self, max_iter, size, beta=1.5, lb = -100, ub = 100): |
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All of this parameters should place in object creation (init function). Don't use the name size, it can be pop_size, n_agents, n_solutions, n_individuals,...)
| Y2_new = Y2 - E*abs(rl*Y2 - Prey[i]) | ||
| y = (Y1_new+Y2_new)/2 | ||
| Prey[i] = y | ||
| t+=1 |
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You need to print out the global best fitness value after each iteration.
Where is your output?
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@oskargirardin Have you fixed my comments yet? |
Hello!
We implemented the Golden Jackal Optimizer (GJO) discussed in the following paper: https://www.sciencedirect.com/science/article/abs/pii/S095741742200358X . This is ready to be tested.
Thanks!
Ian, Oskar and Corentin