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import random as rnd |
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import copy |
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from NiaPy.benchmarks.utility import Utility |
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__all__ = ['ArtificialBeeColonyAlgorithm'] |
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class SolutionABC(object): |
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def __init__(self, D, LB, UB): |
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self.D = D |
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self.Solution = [] |
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self.Fitness = float('inf') |
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self.LB = LB |
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self.UB = UB |
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self.generateSolution() |
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def generateSolution(self): |
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self.Solution = [self.LB + (self.UB - self.LB) * rnd.random() |
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for _i in range(self.D)] |
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def repair(self): |
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for i in range(self.D): |
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if self.Solution[i] > self.UB: |
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View Code Duplication |
self.Solution[i] = self.UB |
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if self.Solution[i] < self.LB: |
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self.Solution[i] = self.LB |
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def evaluate(self): |
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self.Fitness = SolutionABC.FuncEval(self.D, self.Solution) |
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def toString(self): |
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pass |
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class ArtificialBeeColonyAlgorithm(object): |
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"""Artificial Bee Colony algorithm. |
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Date: 12. 2. 2018 |
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Authors : Uros Mlakar |
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License: MIT |
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Reference paper: Karaboga, D., and Bahriye B. "A powerful |
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and efficient algorithm for numerical function optimization: artificial |
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bee colony (ABC) algorithm." Journal of global optimization 39.3 (2007): 459-471. |
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EDITED - TODO: More tests are required! Validation! |
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TODO: EVALUATIONS! |
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""" |
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# pylint: disable=too-many-instance-attributes |
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def __init__(self, D, NP, nFES, benchmark): |
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"""**__init__(self, D, NP, nFES, benchmark)**. |
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Arguments: |
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D {integer} -- dimension of problem |
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NP {integer} -- population size |
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nFES {integer} -- number of function evaluations |
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benchmark {object} -- benchmark implementation object |
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Raises: |
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TypeError -- Raised when given benchmark function which does not exists. |
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""" |
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self.benchmark = Utility().get_benchmark(benchmark) |
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self.D = D |
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self.NP = NP |
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self.FoodNumber = int(self.NP / 2) |
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self.Limit = 100 |
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self.Trial = [] |
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self.Foods = [] |
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self.Probs = [] |
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self.nFES = nFES |
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self.Lower = self.benchmark.Lower |
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self.Upper = self.benchmark.Upper |
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SolutionABC.FuncEval = staticmethod(self.benchmark.function()) |
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self.Best = SolutionABC(self.D, self.Lower, self.Upper) |
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def init(self): |
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self.Probs = [0 for i in range(self.FoodNumber)] |
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self.Trial = [0 for i in range(self.FoodNumber)] |
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for i in range(self.FoodNumber): |
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self.Foods.append(SolutionABC(self.D, self.Lower, self.Upper)) |
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self.Foods[i].evaluate() |
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self.checkForBest(self.Foods[i]) |
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def CalculateProbs(self): |
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self.Probs = [1.0 / (self.Foods[i].Fitness + 0.01) |
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for i in range(self.FoodNumber)] |
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s = sum(self.Probs) |
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self.Probs = [self.Probs[i] / s for i in range(self.FoodNumber)] |
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def checkForBest(self, Solution): |
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if Solution.Fitness <= self.Best.Fitness: |
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self.Best = copy.deepcopy(Solution) |
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def run(self): |
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self.init() |
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FEs = self.FoodNumber |
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while FEs < self.nFES: |
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self.Best.toString() |
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for i in range(self.FoodNumber): |
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newSolution = copy.deepcopy(self.Foods[i]) |
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param2change = int(rnd.random() * self.D) |
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neighbor = int(self.FoodNumber * rnd.random()) |
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newSolution.Solution[param2change] = self.Foods[i].Solution[param2change] \ |
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+ (-1 + 2 * rnd.random()) * \ |
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(self.Foods[i].Solution[param2change] - |
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self.Foods[neighbor].Solution[param2change]) |
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newSolution.repair() |
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newSolution.evaluate() |
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if newSolution.Fitness < self.Foods[i].Fitness: |
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self.checkForBest(newSolution) |
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self.Foods[i] = newSolution |
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self.Trial[i] = 0 |
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else: |
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self.Trial[i] += 1 |
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FEs += self.FoodNumber |
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self.CalculateProbs() |
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t, s = 0, 0 |
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while t < self.FoodNumber: |
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if rnd.random() < self.Probs[s]: |
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t += 1 |
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Solution = copy.deepcopy(self.Foods[s]) |
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param2change = int(rnd.random() * self.D) |
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neighbor = int(self.FoodNumber * rnd.random()) |
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while neighbor == s: |
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neighbor = int(self.FoodNumber * rnd.random()) |
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Solution.Solution[param2change] = self.Foods[s].Solution[param2change] \ |
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+ (-1 + 2 * rnd.random()) * ( |
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self.Foods[s].Solution[param2change] - |
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self.Foods[neighbor].Solution[param2change]) |
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Solution.repair() |
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Solution.evaluate() |
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FEs += 1 |
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if Solution.Fitness < self.Foods[s].Fitness: |
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self.checkForBest(newSolution) |
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self.Foods[s] = Solution |
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self.Trial[s] = 0 |
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else: |
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self.Trial[s] += 1 |
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s += 1 |
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if s == self.FoodNumber: |
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s = 0 |
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mi = self.Trial.index(max(self.Trial)) |
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if self.Trial[mi] >= self.Limit: |
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self.Foods[mi] = SolutionABC(self.D, self.Lower, self.Upper) |
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self.Foods[mi].evaluate() |
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FEs += 1 |
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self.Trial[mi] = 0 |
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return self.Best.Fitness |
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NiaPy
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