NiaPy.algorithms.basic.hho

Complexity

Total Complexity

27

Size/Duplication

Total Lines	211
Duplicated Lines	0 %

Importance

Changes

0

8 Methods

Rating	Name	Duplication	Size	Complexity
F	HarrisHawksOptimization.runIteration()	0	78	19
A	HarrisHawksOptimization.typeParameters()	0	16	2
A	HarrisHawksOptimization.setParameters()	0	11	1
A	HarrisHawksOptimization.initPopulation()	0	16	1
A	HarrisHawksOptimization.__init__()	0	2	1
A	HarrisHawksOptimization.getParameters()	0	11	1
A	HarrisHawksOptimization.levy_function()	0	16	1
A	HarrisHawksOptimization.algorithmInfo()	0	11	1

# encoding=utf8
import logging

from numpy import random as rand, sin, pi, argmin, abs, mean
from scipy.special import gamma

from NiaPy.algorithms.algorithm import Algorithm

logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')

__all__ = ['HarrisHawksOptimization']


class HarrisHawksOptimization(Algorithm):
    r"""Implementation of Harris Hawks Optimization algorithm.

    Algorithm:
            Harris Hawks Optimization

    Date:
            2020

    Authors:
            Francisco Jose Solis-Munoz

    License:
            MIT

    Reference paper:
            Heidari et al. "Harris hawks optimization: Algorithm and applications". Future Generation Computer Systems. 2019. Vol. 97. 849-872.

    Attributes:
            Name (List[str]): List of strings representing algorithm name.
            levy (float): Levy factor.

    See Also:
            * :class:`NiaPy.algorithms.Algorithm`
    """
    Name = ['HarrisHawksOptimization', 'HHO']

    def __init__(self, **kwargs):
        super(HarrisHawksOptimization, self).__init__(**kwargs)

    @staticmethod
    def algorithmInfo():
        r"""Get algorithms information.

        Returns:
                str: Algorithm information.

        See Also:
                * :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
        """
        return r"""Heidari et al. "Harris hawks optimization: Algorithm and applications". Future Generation Computer Systems. 2019. Vol. 97. 849-872."""

    @staticmethod
    def typeParameters():
        r"""Return dict with where key of dict represents parameter name and values represent checking functions for selected parameter.

        Returns:
                Dict[str, Callable]:
                        * levy (Callable[[Union[float, int]], bool]): Levy factor.

        See Also:
                * :func:`NiaPy.algorithms.Algorithm.typeParameters`
        """
        d = Algorithm.typeParameters()
        d.update({
            'levy': lambda x: isinstance(x, (float, int)) and x > 0,
        })
        return d

    def setParameters(self, NP=40, levy=0.01, **ukwargs):
        r"""Set the parameters of the algorithm.

        Args:
                levy (Optional[float]): Levy factor.

        See Also:
                * :func:`NiaPy.algorithms.Algorithm.setParameters`
        """
        Algorithm.setParameters(self, NP=NP, **ukwargs)
        self.levy = levy

    def getParameters(self):
        r"""Get parameters of the algorithm.

        Returns:
                Dict[str, Any]
        """
        d = Algorithm.getParameters(self)
        d.update({
            'levy': self.levy
        })
        return d

    def initPopulation(self, task, rnd=rand):
        r"""Initialize the starting population.

        Parameters:
                task (Task): Optimization task

        Returns:
                Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
                        1. New population.
                        2. New population fitness/function values.

        See Also:
                * :func:`NiaPy.algorithms.Algorithm.initPopulation`
        """
        Sol, Fitness, d = Algorithm.initPopulation(self, task)
        return Sol, Fitness, d

    def levy_function(self, dims, step=0.01, rnd=rand):
        r"""Calculate levy function.

        Parameters:
                dim (int): Number of dimensions
                step (float): Step of the Levy function

        Returns:
                float: The Levy function evaluation
        """
        beta = 1.5
        sigma = (gamma(1 + beta) * sin(pi * beta / 2) / (gamma((1 + beta / 2) * beta * 2.0 ** ((beta - 1) / 2)))) ** (1 / beta)
        normal_1 = rnd.normal(0, sigma, size=dims)
        normal_2 = rnd.normal(0, 1, size=dims)
        result = step * normal_1 / (abs(normal_2) ** (1 / beta))
        return result

    def runIteration(self, task, Sol, Fitness, xb, fxb, **dparams):
        r"""Core function of Harris Hawks Optimization.

        Parameters:
                task (Task): Optimization task.
                Sol (numpy.ndarray): Current population
                Fitness (numpy.ndarray[float]): Current population fitness/funciton values
                xb (numpy.ndarray): Current best individual
                fxb (float): Current best individual function/fitness value
                dparams (Dict[str, Any]): Additional algorithm arguments

        Returns:
                Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
                        1. New population
                        2. New population fitness/function vlues
                        3. New global best solution
                        4. New global best fitness/objective value
        """
        # Decreasing energy factor
        decreasing_energy_factor = 2 * (1 - task.iters() / task.nGEN)
        mean_sol = mean(Sol)
        # Update population
        for i in range(self.NP):
            jumping_energy = self.Rand.uniform(0, 2)
            decreasing_energy_random = self.Rand.uniform(-1, 1)
            escaping_energy = decreasing_energy_factor * decreasing_energy_random
            escaping_energy_abs = abs(escaping_energy)
            random_number = self.Rand.rand()
            if escaping_energy >= 1 and random_number >= 0.5:
                # 0. Exploration: Random tall tree
                rhi = self.Rand.randint(0, self.NP)
                random_agent = Sol[rhi]
                Sol[i] = random_agent - self.Rand.rand() * abs(random_agent - 2 * self.Rand.rand() * Sol[i])
            elif escaping_energy_abs >= 1 and random_number < 0.5:
                # 1. Exploration: Family members mean
                Sol[i] = (xb - mean_sol) - self.Rand.rand() * self.Rand.uniform(task.Lower, task.Upper)
            elif escaping_energy_abs >= 0.5 and random_number >= 0.5:
                # 2. Exploitation: Soft besiege
                Sol[i] = \
                    (xb - Sol[i]) - \
                    escaping_energy * \
                    abs(jumping_energy * xb - Sol[i])
            elif escaping_energy_abs < 0.5 and random_number >= 0.5:
                # 3. Exploitation: Hard besiege
                Sol[i] = \
                    xb - \
                    escaping_energy * \
                    abs(xb - Sol[i])
            elif escaping_energy_abs >= 0.5 and random_number < 0.5:
                # 4. Exploitation: Soft besiege with pprogressive rapid dives
                cand1 = task.repair(xb - escaping_energy * abs(jumping_energy * xb - Sol[i]), rnd=self.Rand)
                random_vector = self.Rand.rand(task.D)
                cand2 = task.repair(cand1 + random_vector * self.levy_function(task.D, self.levy, rnd=self.Rand), rnd=self.Rand)
                if task.eval(cand1) < Fitness[i]:
                    Sol[i] = cand1
                elif task.eval(cand2) < Fitness[i]:
                    Sol[i] = cand2
            elif escaping_energy_abs < 0.5 and random_number < 0.5:
                # 5. Exploitation: Hard besiege with progressive rapid dives
                cand1 = task.repair(xb - escaping_energy * abs(jumping_energy * xb - mean_sol), rnd=self.Rand)
                random_vector = self.Rand.rand(task.D)
                cand2 = task.repair(cand1 + random_vector * self.levy_function(task.D, self.levy, rnd=self.Rand), rnd=self.Rand)
                if task.eval(cand1) < Fitness[i]:
                    Sol[i] = cand1
                elif task.eval(cand2) < Fitness[i]:
                    Sol[i] = cand2
            # Repair agent (from population) values
            Sol[i] = task.repair(Sol[i], rnd=self.Rand)
            # Eval population
            Fitness[i] = task.eval(Sol[i])
        # Get best of population
        best_index = argmin(Fitness)
        xb_cand = Sol[best_index].copy()
        fxb_cand = Fitness[best_index].copy()
        if fxb_cand < fxb:
            fxb = fxb_cand
            xb = xb_cand.copy()
        return Sol, Fitness, xb, fxb, {}

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