#!/usr/bin/env python
# Created by "Thieu" at 07:03, 16/07/2025 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
from mealpy.utils.opt_info import OptInfo
[docs]class OriginalESO(Optimizer):
"""
The original version of: Electrical Storm Optimization (ESO)
Parameters
----------
epoch : int
Maximum number of iterations, in range [1, 100000]. Default is 10000.
pop_size : int
Number of population size, in range [5, 10000]. Default is 100.
References
~~~~~~~~~~
1. Soto Calvo, Manuel, and Han Soo Lee. 2025.
"Electrical Storm Optimization (ESO) Algorithm: Theoretical Foundations, Analysis, and Application to Engineering Problems"
Machine Learning and Knowledge Extraction 7, no. 1: 24. https://doi.org/10.3390/make7010024
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, ESO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = ESO.OriginalESO(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}")
"""
OPT_INFO = OptInfo(name="Electrical Storm Optimization", year=2025, difficulty="hard", kind="original")
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
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
self.is_parallelizable = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## Calculate storm parameters
# Calculate field resistance based on population spread
pos_pop = np.array([agent.solution for agent in self.pop])
mean_pos = np.mean(pos_pop, axis=0)
std_pos = np.sqrt(np.mean(np.sum((pos_pop - mean_pos)**2, axis=1)))
# std_pos = np.std(pos_pop, axis=0)
peak_to_peak = np.max(np.max(pos_pop, axis=0) - np.min(pos_pop, axis=0))
# Field resistance
if peak_to_peak <= 0:
resistance = 0
ionized_pop = []
else:
resistance = std_pos / peak_to_peak
# Identify ionized areas (promising regions)
# Calculate percentile threshold
percentile_threshold = (resistance / 2) * 100
# Find solutions better than percentile
fits = np.array([agent.target.fitness for agent in self.pop])
fitness_percentile = np.percentile(fits, percentile_threshold)
ionized_indices = np.where(fits <= fitness_percentile)[0]
ionized_pop = [self.pop[idx] for idx in ionized_indices]
# Calculate field conductivity using logistic function
if resistance <= 0:
fc = 1.0
else:
# Beta calculation (logistic function)
try:
exp_term = np.exp(resistance) / resistance
log_term = np.log(1. - resistance) if resistance < 1 else 0
beta = 1. / (1. + np.exp(-exp_term) * (resistance - abs(log_term)))
except (OverflowError, ValueError):
beta = 0.5
try:
fc = (np.exp(resistance) + np.exp(1 - resistance) * abs(np.log(resistance)) * beta)
except (OverflowError, ValueError):
fc = 1.0
# Calculate field intensity using logistic function
if resistance <= 0:
fi = fc
else:
# Gamma calculation
try:
exp_term = np.exp(resistance) / resistance
iter_ratio = epoch / self.epoch
log_term = np.log(1 - iter_ratio) if iter_ratio < 1 else 0
gama = 1 / (1 + np.exp(-exp_term * (resistance - abs(log_term))))
except (OverflowError, ValueError):
gama = 0.5
fi = fc * gama
# Calculate storm power
if fc > 0:
storm_power = (resistance * fi) / fc
else:
storm_power = 0
# Update each lighting agent
pop_new = []
for idx in range(0, self.pop_size):
# Initialize new lighting position
if idx == 0 or len(ionized_pop) == 0:
agent = self.generate_empty_agent()
else:
# Initialize near ionized areas
alpha = ionized_pop[self.generator.integers(0, len(ionized_pop))]
perturbation = self.generator.normal(loc=0, scale=storm_power, size=self.problem.n_dims)
pos_new = alpha.solution + perturbation
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
agent.target = self.get_target(agent.solution)
## Branching and propagation
# Simulate branching and propagation of lightning
in_ionized = False
for alpha in ionized_pop:
if np.linalg.norm(agent.solution - alpha.solution) < 0.1:
in_ionized = True
break
if in_ionized:
# Propagate within ionized area
pos_new = agent.solution * storm_power
else:
# Propagate towards ionized areas
if len(ionized_pop) > 0:
# Average position of ionized areas
avg_ionized = np.mean([agent.solution for agent in ionized_pop], axis=0)
# Random perturbation
pos_new = avg_ionized + storm_power * np.exp(fc) * self.generator.uniform(-fc, fc, self.problem.n_dims)
else:
# Random search
pos_new = self.generator.uniform(self.problem.lb, self.problem.ub, self.problem.n_dims)
pos_new = self.correct_solution(pos_new)
agent_new = self.generate_agent(pos_new)
# Select better position
if self.compare_target(agent_new.target, agent.target):
pop_new.append(agent_new)
else:
pop_new.append(agent)
self.pop = pop_new