NiaOrg /
NiaPy
| @@ 26-69 (lines=44) @@ | ||
| 23 | ||
| 24 | # pylint: disable=too-many-instance-attributes |
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| 25 | ||
| 26 | def __init__(self, D, NP, nFES, A, r, Qmin, Qmax, benchmark): |
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| 27 | """Initialize algorithm. |
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| 28 | ||
| 29 | Arguments: |
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| 30 | D {integer} -- dimension of problem |
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| 31 | NP {integer} -- population size |
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| 32 | nFES {integer} -- number of function evaluations |
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| 33 | A {decimal} -- loudness |
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| 34 | r {decimal} -- pulse rate |
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| 35 | Qmin {decimal} -- minimum frequency |
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| 36 | Qmax {decimal } -- maximum frequency |
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| 37 | benchmark {object} -- benchmark implementation object |
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| 38 | ||
| 39 | Raises: |
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| 40 | TypeError -- Raised when given benchmark function which does not exists. |
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| 41 | ||
| 42 | """ |
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| 43 | ||
| 44 | self.benchmark = Utility().get_benchmark(benchmark) |
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| 45 | self.D = D # dimension |
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| 46 | self.NP = NP # population size |
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| 47 | self.nFES = nFES # number of function evaluations |
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| 48 | self.A = A # loudness |
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| 49 | self.r = r # pulse rate |
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| 50 | self.Qmin = Qmin # frequency min |
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| 51 | self.Qmax = Qmax # frequency max |
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| 52 | self.Lower = self.benchmark.Lower # lower bound |
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| 53 | self.Upper = self.benchmark.Upper # upper bound |
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| 54 | ||
| 55 | self.f_min = 0.0 # minimum fitness |
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| 56 | ||
| 57 | self.Lb = [0] * self.D # lower bound |
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| 58 | self.Ub = [0] * self.D # upper bound |
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| 59 | self.Q = [0] * self.NP # frequency |
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| 60 | ||
| 61 | self.v = [[0 for _i in range(self.D)] |
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| 62 | for _j in range(self.NP)] # velocity |
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| 63 | self.Sol = [[0 for _i in range(self.D)] for _j in range( |
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| 64 | self.NP)] # population of solutions |
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| 65 | self.Fitness = [0] * self.NP # fitness |
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| 66 | self.best = [0] * self.D # best solution |
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| 67 | self.evaluations = 0 # evaluations counter |
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| 68 | self.eval_flag = True # evaluations flag |
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| 69 | self.Fun = self.benchmark.function() |
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| 70 | ||
| 71 | def best_bat(self): |
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| 72 | """Find best bat.""" |
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| @@ 23-51 (lines=29) @@ | ||
| 20 | class HybridBatAlgorithm(object): |
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| 21 | # pylint: disable=too-many-instance-attributes |
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| 22 | ||
| 23 | def __init__(self, D, NP, nFES, A, r, F, CR, Qmin, Qmax, benchmark): |
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| 24 | self.benchmark = Utility().get_benchmark(benchmark) |
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| 25 | self.D = D # dimension |
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| 26 | self.NP = NP # population size |
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| 27 | self.nFES = nFES # number of function evaluations |
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| 28 | self.A = A # loudness |
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| 29 | self.r = r # pulse rate |
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| 30 | self.F = F # scaling factor |
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| 31 | self.CR = CR # crossover rate |
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| 32 | self.Qmin = Qmin # frequency min |
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| 33 | self.Qmax = Qmax # frequency max |
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| 34 | self.Lower = self.benchmark.Lower # lower bound |
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| 35 | self.Upper = self.benchmark.Upper # upper bound |
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| 36 | self.eval_flag = True # evaluations flag |
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| 37 | self.Fun = self.benchmark.function() |
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| 38 | ||
| 39 | self.f_min = 0.0 # minimum fitness |
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| 40 | ||
| 41 | self.Lb = [0] * self.D # lower bound |
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| 42 | self.Ub = [0] * self.D # upper bound |
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| 43 | self.Q = [0] * self.NP # frequency |
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| 44 | ||
| 45 | self.v = [[0 for _i in range(self.D)] |
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| 46 | for _j in range(self.NP)] # velocity |
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| 47 | self.Sol = [[0 for _i in range(self.D)] for _j in range( |
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| 48 | self.NP)] # population of solutions |
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| 49 | self.Fitness = [0] * self.NP # fitness |
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| 50 | self.best = [0] * self.D # best solution |
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| 51 | self.evaluations = 0 # evaluations counter |
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| 52 | ||
| 53 | def best_bat(self): |
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| 54 | i = 0 |
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