NiaOrg /
NiaPy
| @@ 81-96 (lines=16) @@ | ||
| 78 | j = i |
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| 79 | for i in range(self.D): |
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| 80 | self.best[i] = self.Sol[j][i] |
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| 81 | self.f_min = self.Fitness[j] |
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| 82 | ||
| 83 | def eval_true(self): |
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| 84 | """Check evauations.""" |
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| 85 | ||
| 86 | if self.evaluations == self.nFES: |
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| 87 | self.eval_flag = False |
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| 88 | ||
| 89 | def init_bat(self): |
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| 90 | """Initialize bat.""" |
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| 91 | ||
| 92 | for i in range(self.D): |
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| 93 | self.Lb[i] = self.Lower |
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| 94 | self.Ub[i] = self.Upper |
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| 95 | ||
| 96 | for i in range(self.NP): |
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| 97 | self.Q[i] = 0 |
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| 98 | for j in range(self.D): |
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| 99 | rnd = random.uniform(0, 1) |
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| @@ 61-74 (lines=14) @@ | ||
| 58 | j = i |
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| 59 | for i in range(self.D): |
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| 60 | self.best[i] = self.Sol[j][i] |
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| 61 | self.f_min = self.Fitness[j] |
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| 62 | ||
| 63 | def eval_true(self): |
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| 64 | """Check evauations.""" |
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| 65 | ||
| 66 | if self.evaluations == self.nFES: |
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| 67 | self.eval_flag = False |
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| 68 | ||
| 69 | def init_bat(self): |
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| 70 | for i in range(self.D): |
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| 71 | self.Lb[i] = self.Lower |
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| 72 | self.Ub[i] = self.Upper |
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| 73 | ||
| 74 | for i in range(self.NP): |
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| 75 | self.Q[i] = 0 |
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| 76 | for j in range(self.D): |
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| 77 | rnd = random.uniform(0, 1) |
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| @@ 67-79 (lines=13) @@ | ||
| 64 | def best_flower(self): |
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| 65 | i = 0 |
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| 66 | j = 0 |
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| 67 | for i in range(self.NP): |
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| 68 | if self.Fitness[i] < self.Fitness[j]: |
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| 69 | j = i |
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| 70 | for i in range(self.D): |
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| 71 | self.best[i] = self.Sol[j][i] |
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| 72 | self.f_min = self.Fitness[j] |
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| 73 | ||
| 74 | @classmethod |
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| 75 | def simplebounds(cls, val, lower, upper): |
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| 76 | if val < lower: |
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| 77 | val = lower |
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| 78 | if val > upper: |
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| 79 | val = upper |
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| 80 | return val |
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| 81 | ||
| 82 | def init_flower(self): |
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