#!/usr/bin/env python
# Created by "Thieu" at 18:29, 11/03/2023 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalNGO(Optimizer):
"""
The original version of: Northern Goshawk Optimization (NGO)
Links:
1. https://ieeexplore.ieee.org/abstract/document/9638618
2. https://www.mathworks.com/matlabcentral/fileexchange/106665-northern-goshawk-optimization-a-new-swarm-based-algorithm
Notes:
1. This is somewhat concerning, as there appears to be a high degree of similarity between the source code for this algorithm and the Pelican Optimization Algorithm (POA).
2. Algorithm design is similar similar to Zebra Optimization Algorithm (ZOA), Osprey Optimization Algorithm (OOA), Coati Optimization Algorithm (CoatiOA), Siberian Tiger Optimization (STO), Language Education Optimization (LEO), Serval Optimization Algorithm (SOA), Walrus Optimization Algorithm (WOA), Fennec Fox Optimization (FFO), Three-periods optimization algorithm (TPOA), Teamwork optimization algorithm (TOA), Pelican Optimization Algorithm (POA), Tasmanian devil optimization (TDO), Archery algorithm (AA), Cat and mouse based optimizer (CMBO)
3. It may be useful to compare the Matlab code of this algorithm with those of the similar algorithms to ensure its accuracy and completeness.
4. The article may share some similarities with previous work by the same authors, further investigation may be warranted to verify the benchmark results reported in the papers and ensure their reliability and accuracy.
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, NGO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = NGO.OriginalNGO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
References
~~~~~~~~~~
[1] Dehghani, M., Hubálovský, Š., & Trojovský, P. (2021). Northern goshawk optimization: a new swarm-based
algorithm for solving optimization problems. IEEE Access, 9, 162059-162080.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [5, 10000])
self.set_parameters(["epoch", "pop_size"])
self.is_parallelizable = False
self.sort_flag = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## UPDATE Northern goshawks based on PHASE1 and PHASE2
for idx in range(0, self.pop_size):
# Phase 1: Exploration
kk = self.generator.permutation(self.pop_size)[0]
if self.compare_target(self.pop[kk].target, self.pop[idx].target): # Eq. 4
pos_new = self.pop[idx].solution + self.generator.random(self.problem.n_dims) * (self.pop[kk].solution - self.generator.integers(1, 3) * self.pop[idx].solution)
else:
pos_new = self.pop[idx].solution + self.generator.random(self.problem.n_dims) * (self.pop[idx].solution - self.pop[kk].solution)
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
if self.compare_target(agent.target, self.pop[idx].target, self.problem.minmax):
self.pop[idx] = agent
# PHASE 2 Exploitation
R = 0.02 * (1. - epoch / self.epoch) # Eq. 6
pos_new = self.pop[idx].solution + (-R + 2 * R * self.generator.random(self.problem.n_dims)) * self.pop[idx].solution # Eq. 7
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
if self.compare_target(agent.target, self.pop[idx].target, self.problem.minmax):
self.pop[idx] = agent