NiaPy/algorithms/basic/ba.py 1 location
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@@ 81-96 (lines=16) @@
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| 78 |
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self.f_min = self.Fitness[j] |
| 79 |
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| 80 |
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def init_bat(self): |
| 81 |
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"""Initialize bat.""" |
| 82 |
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| 83 |
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for i in range(self.D): |
| 84 |
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self.Lb[i] = self.Lower |
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self.Ub[i] = self.Upper |
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| 87 |
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for i in range(self.NP): |
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self.Q[i] = 0 |
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for j in range(self.D): |
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rnd = random.uniform(0, 1) |
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self.v[i][j] = 0.0 |
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self.Sol[i][j] = self.Lb[j] + (self.Ub[j] - self.Lb[j]) * rnd |
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self.Fitness[i] = self.Fun(self.D, self.Sol[i]) |
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self.evaluations = self.evaluations + 1 |
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self.best_bat() |
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@classmethod |
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def simplebounds(cls, val, lower, upper): |
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if val < lower: |
NiaPy/algorithms/modified/hba.py 1 location
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@@ 61-74 (lines=14) @@
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| 58 |
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for i in range(self.D): |
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self.best[i] = self.Sol[j][i] |
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self.f_min = self.Fitness[j] |
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| 62 |
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def init_bat(self): |
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for i in range(self.D): |
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self.Lb[i] = self.Lower |
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self.Ub[i] = self.Upper |
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for i in range(self.NP): |
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self.Q[i] = 0 |
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for j in range(self.D): |
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rnd = random.uniform(0, 1) |
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self.v[i][j] = 0.0 |
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self.Sol[i][j] = self.Lb[j] + (self.Ub[j] - self.Lb[j]) * rnd |
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self.Fitness[i] = self.Fun(self.D, self.Sol[i]) |
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self.evaluations = self.evaluations + 1 |
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self.best_bat() |
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@classmethod |
NiaPy/algorithms/basic/fpa.py 1 location
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@@ 67-79 (lines=13) @@
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| 64 |
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if val > upper: |
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val = upper |
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return val |
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def init_flower(self): |
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for i in range(self.D): |
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self.Lb[i] = self.Lower |
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self.Ub[i] = self.Upper |
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for i in range(self.NP): |
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for j in range(self.D): |
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rnd = random.uniform(0, 1) |
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self.dS[i][j] = 0.0 |
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self.Sol[i][j] = self.Lb[j] + (self.Ub[j] - self.Lb[j]) * rnd |
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self.Fitness[i] = self.Fun(self.D, self.Sol[i]) |
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self.evaluations = self.evaluations + 1 |
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self.best_flower() |
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def move_flower(self): |
NiaOrg /
NiaPy
