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# encoding=utf8 |
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# pylint: disable=mixed-indentation, trailing-whitespace, multiple-statements, attribute-defined-outside-init, logging-not-lazy, no-self-use, line-too-long, arguments-differ, bad-continuation |
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import logging |
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from numpy import apply_along_axis, zeros, argsort, concatenate, array, exp, cos, pi |
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from NiaPy.algorithms.algorithm import Algorithm |
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logging.basicConfig() |
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logger = logging.getLogger('NiaPy.algorithms.basic') |
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logger.setLevel('INFO') |
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__all__ = ['MothFlameOptimizer'] |
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class MothFlameOptimizer(Algorithm): |
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r"""MothFlameOptimizer of Moth flame optimizer. |
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Algorithm: |
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Moth flame optimizer |
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Date: |
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2018 |
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Author: |
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Kivanc Guckiran and Klemen Berkovič |
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License: |
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MIT |
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Reference paper: |
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Mirjalili, Seyedali. "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm." Knowledge-Based Systems 89 (2015): 228-249. |
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Attributes: |
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Name (List[str]): List of strings representing algorithm name. |
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See Also: |
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* :class:`NiaPy.algorithms.algorithm.Algorithm` |
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""" |
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Name = ['MothFlameOptimizer', 'MFO'] |
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@staticmethod |
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def algorithmInfo(): |
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r"""Get basic information of algorithm. |
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Returns: |
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str: Basic information. |
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See Also: |
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* :func:`NiaPy.algorithms.Algorithm.algorithmInfo` |
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""" |
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return r"""Mirjalili, Seyedali. "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm." Knowledge-Based Systems 89 (2015): 228-249.""" |
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@staticmethod |
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def typeParameters(): |
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r"""Get dictionary with functions for checking values of parameters. |
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Returns: |
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Dict[str, Callable]: TODO |
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See Also: |
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* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters` |
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""" |
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return Algorithm.typeParameters() |
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def setParameters(self, NP=25, **ukwargs): |
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r"""Set the algorithm parameters. |
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Arguments: |
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NP (int): Number of individuals in population |
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See Also: |
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* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters` |
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""" |
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Algorithm.setParameters(self, NP=NP, **ukwargs) |
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def initPopulation(self, task): |
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r"""Initialize starting population. |
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Args: |
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task (Task): Optimization task |
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Returns: |
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Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]: |
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1. Initialized population |
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2. Initialized population function/fitness values |
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3. Additional arguments: |
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* best_flames (numpy.ndarray): Best individuals |
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* best_flame_fitness (numpy.ndarray): Best individuals fitness/function values |
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* previous_population (numpy.ndarray): Previous population |
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* previous_fitness (numpy.ndarray[float]): Previous population fitness/function values |
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See Also: |
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* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation` |
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""" |
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moth_pos, moth_fitness, d = Algorithm.initPopulation(self, task) |
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# Create best population |
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indexes = argsort(moth_fitness) |
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best_flames, best_flame_fitness = moth_pos[indexes], moth_fitness[indexes] |
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# Init previous population |
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previous_population, previous_fitness = zeros((self.NP, task.D)), zeros(self.NP) |
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d.update({'best_flames': best_flames, 'best_flame_fitness': best_flame_fitness, 'previous_population': previous_population, 'previous_fitness': previous_fitness}) |
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return moth_pos, moth_fitness, d |
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def runIteration(self, task, moth_pos, moth_fitness, xb, fxb, best_flames, best_flame_fitness, previous_population, previous_fitness, **dparams): |
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r"""Core function of MothFlameOptimizer algorithm. |
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Args: |
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task (Task): Optimization task. |
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moth_pos (numpy.ndarray): Current population. |
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moth_fitness (numpy.ndarray): Current population fitness/function values. |
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xb (numpy.ndarray): Current population best individual. |
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fxb (float): Current best individual |
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best_flames (numpy.ndarray): Best found individuals |
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best_flame_fitness (numpy.ndarray): Best found individuals fitness/function values |
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previous_population (numpy.ndarray): Previous population |
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previous_fitness (numpy.ndarray): Previous population fitness/function values |
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**dparams (Dict[str, Any]): Additional parameters |
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Returns: |
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Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]: |
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1. New population. |
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2. New population fitness/function values. |
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3. New global best solution |
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4. New global best fitness/objective value |
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5. Additional arguments: |
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* best_flames (numpy.ndarray): Best individuals. |
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* best_flame_fitness (numpy.ndarray): Best individuals fitness/function values. |
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* previous_population (numpy.ndarray): Previous population. |
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* previous_fitness (numpy.ndarray): Previous population fitness/function values. |
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""" |
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# Previous positions |
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previous_population, previous_fitness = moth_pos, moth_fitness |
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# Create sorted population |
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indexes = argsort(moth_fitness) |
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sorted_population = moth_pos[indexes] |
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# Some parameters |
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flame_no, a = round(self.NP - task.Iters * ((self.NP - 1) / task.nGEN)), -1 + task.Iters * ((-1) / task.nGEN) |
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for i in range(self.NP): |
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for j in range(task.D): |
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distance_to_flame, b, t = abs(sorted_population[i, j] - moth_pos[i, j]), 1, (a - 1) * self.rand() + 1 |
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if i <= flame_no: moth_pos[i, j] = distance_to_flame * exp(b * t) * cos(2 * pi * t) + sorted_population[i, j] |
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else: moth_pos[i, j] = distance_to_flame * exp(b * t) * cos(2 * pi * t) + sorted_population[flame_no, j] |
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moth_pos = apply_along_axis(task.repair, 1, moth_pos, self.Rand) |
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moth_fitness = apply_along_axis(task.eval, 1, moth_pos) |
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xb, fxb = self.getBest(moth_pos, moth_fitness, xb, fxb) |
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double_population, double_fitness = concatenate((previous_population, best_flames), axis=0), concatenate((previous_fitness, best_flame_fitness), axis=0) |
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indexes = argsort(double_fitness) |
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double_sorted_fitness, double_sorted_population = double_fitness[indexes], double_population[indexes] |
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for newIdx in range(2 * self.NP): double_sorted_population[newIdx] = array(double_population[indexes[newIdx], :]) |
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best_flame_fitness, best_flames = double_sorted_fitness[:self.NP], double_sorted_population[:self.NP] |
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return moth_pos, moth_fitness, xb, fxb, {'best_flames': best_flames, 'best_flame_fitness': best_flame_fitness, 'previous_population': previous_population, 'previous_fitness': previous_fitness} |
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# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
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NiaOrg /
NiaPy
