#!/usr/bin/env python
# Created by "Thieu" at 17:41, 21/05/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalWarSO(Optimizer):
"""
The original version of: War Strategy Optimization (WarSO) algorithm
Links:
1. https://www.researchgate.net/publication/358806739_War_Strategy_Optimization_Algorithm_A_New_Effective_Metaheuristic_Algorithm_for_Global_Optimization
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ rr (float): [0.1, 0.9], the probability of switching position updating, default=0.1
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, WarSO
>>>
>>> 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 = WarSO.OriginalWarSO(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] Ayyarao, Tummala SLV, and Polamarasetty P. Kumar. "Parameter estimation of solar PV models with a new proposed
war strategy optimization algorithm." International Journal of Energy Research (2022).
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, rr: float = 0.1, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
rr (float): the probability of switching position updating, default=0.1
"""
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.rr = self.validator.check_float("rr", rr, (0.0, 1.0))
self.set_parameters(["epoch", "pop_size", "rr"])
self.is_parallelizable = False
self.sort_flag = False
[docs] def initialize_variables(self):
self.wl = 2 * np.ones(self.pop_size)
self.wg = np.zeros(self.pop_size)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_sorted = self.get_sorted_population(self.pop, self.problem.minmax)
com = self.generator.permutation(self.pop_size)
for idx in range(0, self.pop_size):
r1 = self.generator.random()
if r1 < self.rr:
pos_new = 2*r1*(self.g_best.solution - self.pop[com[idx]].solution) + \
self.wl[idx]*self.generator.random()*(pop_sorted[idx].solution - self.pop[idx].solution)
else:
pos_new = 2*r1*(pop_sorted[idx].solution - self.g_best.solution) + \
self.generator.random() * (self.wl[idx] * self.g_best.solution - self.pop[idx].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
self.wg[idx] += 1
self.wl[idx] = 1 * self.wl[idx] * (1 - self.wg[idx] / self.epoch)**2