NiaOrg /
NiaPy
| @@ 27-48 (lines=22) @@ | ||
| 24 | ||
| 25 | class FlowerPollinationAlgorithm(object): |
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| 26 | # pylint: disable=too-many-instance-attributes |
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| 27 | def __init__(self, D, NP, nFES, p, benchmark): |
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| 28 | self.benchmark = Utility.get_benchmark(benchmark) |
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| 29 | self.D = D # dimension |
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| 30 | self.NP = NP # population size |
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| 31 | self.nFES = nFES # number of function evaluations |
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| 32 | self.p = p # probability switch |
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| 33 | self.Lower = self.benchmark.Lower # lower bound |
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| 34 | self.Upper = self.benchmark.Upper # upper bound |
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| 35 | self.Fun = self.benchmark.function() # function |
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| 36 | ||
| 37 | self.f_min = 0.0 # minimum fitness |
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| 38 | ||
| 39 | self.Lb = [0] * self.D # lower bound |
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| 40 | self.Ub = [0] * self.D # upper bound |
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| 41 | ||
| 42 | self.dS = [[0 for _i in range(self.D)] |
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| 43 | for _j in range(self.NP)] # differential |
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| 44 | self.Sol = [[0 for _i in range(self.D)] |
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| 45 | for _j in range(self.NP)] # population of solutions |
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| 46 | self.Fitness = [0] * self.NP # fitness |
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| 47 | self.best = [0] * self.D # best solution |
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| 48 | self.evaluations = 0 # evaluations counter |
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| 49 | ||
| 50 | def best_flower(self): |
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| 51 | i = 0 |
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| @@ 23-44 (lines=22) @@ | ||
| 20 | """Firefly Algorithm implementation.""" |
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| 21 | ||
| 22 | # pylint: disable=too-many-instance-attributes |
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| 23 | def __init__(self, D, NP, nFES, alpha, betamin, gamma, benchmark): |
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| 24 | self.benchmark = Utility.get_benchmark(benchmark) |
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| 25 | self.D = D # dimension of the problem |
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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.alpha = alpha # alpha parameter |
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| 29 | self.betamin = betamin # beta parameter |
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| 30 | self.gamma = gamma # gamma parameter |
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| 31 | # sort of fireflies according to fitness value |
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| 32 | self.Index = [0] * self.NP |
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| 33 | self.Fireflies = [[0 for _i in range(self.D)] |
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| 34 | for _j in range(self.NP)] # firefly agents |
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| 35 | self.Fireflies_tmp = [[0 for _i in range(self.D)] for _j in range( |
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| 36 | self.NP)] # intermediate pop |
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| 37 | self.Fitness = [0.0] * self.NP # fitness values |
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| 38 | self.Intensity = [0.0] * self.NP # light intensity |
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| 39 | self.nbest = [0.0] * self.NP # the best solution found so far |
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| 40 | self.Lower = self.benchmark.Lower # lower bound |
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| 41 | self.Upper = self.benchmark.Upper # upper bound |
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| 42 | self.fbest = None # the best |
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| 43 | self.evaluations = 0 |
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| 44 | self.Fun = self.benchmark.function() |
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| 45 | ||
| 46 | def init_ffa(self): |
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| 47 | for i in range(self.NP): |
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