#!/usr/bin/env python
# Created by "Thieu" at 00:08, 27/10/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalGJO(Optimizer):
"""
The original version of: Golden jackal optimization (GJO)
Links:
1. https://www.sciencedirect.com/science/article/abs/pii/S095741742200358X
2. https://www.mathworks.com/matlabcentral/fileexchange/108889-golden-jackal-optimization-algorithm
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, GJO
>>>
>>> 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 = GJO.OriginalGJO(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] Chopra, N., & Ansari, M. M. (2022). Golden jackal optimization: A novel nature-inspired
optimizer for engineering applications. Expert Systems with Applications, 198, 116924.
"""
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.sort_flag = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
E1 = 1.5*(1.-(epoch/self.epoch))
RL = self.get_levy_flight_step(beta=1.5, multiplier=0.05, size=(self.pop_size, self.problem.n_dims), case=-1)
_, (male, female), _ = self.get_special_agents(self.pop, n_best=2, n_worst=1, minmax=self.problem.minmax)
pop_new = []
for idx in range(0, self.pop_size):
male_pos = male.solution.copy()
female_pos = female.solution.copy()
for jdx in range(0, self.problem.n_dims):
r1 = self.generator.random()
E0 = 2*r1 - 1
E = E1 * E0
if np.abs(E) < 1: # EXPLOITATION
t1 = np.abs( (RL[idx, jdx] * male.solution[jdx] - self.pop[idx].solution[jdx]) )
male_pos[jdx] = male.solution[jdx] - E*t1
t2 = np.abs( (RL[idx, jdx] * female.solution[jdx] - self.pop[idx].solution[jdx]) )
female_pos[jdx] = female.solution[jdx] - E*t2
else: # EXPLORATION
t1 = np.abs((male.solution[jdx] - RL[idx, jdx] * self.pop[idx].solution[jdx]))
male_pos[jdx] = male.solution[jdx] - E * t1
t2 = np.abs((female.solution[jdx] - RL[idx, jdx] * self.pop[idx].solution[jdx]))
female_pos[jdx] = female.solution[jdx] - E * t2
pos_new = (male_pos + female_pos) / 2
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = agent
if self.mode in self.AVAILABLE_MODES:
self.pop = self.update_target_for_population(pop_new)