#!/usr/bin/env python
# Created by "Thieu" at 12:51, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalBBOA(Optimizer):
"""
The original version of: Brown-Bear Optimization Algorithm (BBOA)
Links:
1. https://www.mathworks.com/matlabcentral/fileexchange/125490-brown-bear-optimization-algorithm
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, BBOA
>>>
>>> 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 = BBOA.OriginalBBOA(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] Prakash, T., Singh, P. P., Singh, V. P., & Singh, S. N. (2023). A Novel Brown-bear Optimization
Algorithm for Solving Economic Dispatch Problem. In Advanced Control & Optimization Paradigms for
Energy System Operation and Management (pp. 137-164). River Publishers.
"""
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
"""
pp = epoch / self.epoch
## Pedal marking behaviour
pop_new = []
for idx in range(0, self.pop_size):
if pp <= epoch/3: # Gait while walking
pos_new = self.pop[idx].solution + (-pp * self.generator.random(self.problem.n_dims) * self.pop[idx].solution)
elif epoch/3 < pp <= 2*epoch/3: # Careful Stepping
qq = pp * self.generator.random(self.problem.n_dims)
pos_new = self.pop[idx].solution + (qq * (self.g_best.solution - self.generator.integers(1, 3) * self.g_worst.solution))
else:
ww = 2 * pp * np.pi * self.generator.random(self.problem.n_dims)
pos_new = self.pop[idx].solution + (ww*self.g_best.solution - np.abs(self.pop[idx].solution)) - (ww*self.g_worst.solution - np.abs(self.pop[idx].solution))
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] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)
## Sniffing of pedal marks
pop_new = []
for idx in range(0, self.pop_size):
kk = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
if self.compare_target(self.pop[idx].target, self.pop[kk].target, self.problem.minmax):
pos_new = self.pop[idx].solution + self.generator.random() * (self.pop[idx].solution - self.pop[kk].solution)
else:
pos_new = self.pop[idx].solution + self.generator.random() * (self.pop[kk].solution - self.pop[idx].solution)
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] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)