#!/usr/bin/env python
# Created by "https://github.com/fatmasenagul" at 2024
# ---------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
from mealpy.utils.opt_info import OptInfo
[docs]class OriginalChOA(Optimizer):
"""
The original version of: Chimp Optimization Algorithm (ChOA)
Parameters
----------
epoch : int
Maximum number of iterations, default = 10000.
pop_size : int
Number of population size, default = 100.
References
----------
1. Khishe, M. and Mosavi, M.R., 2020. Chimp optimization algorithm.
Expert systems with applications, 149, p.113338. https://doi.org/10.1016/j.eswa.2020.113338
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, ChOA
>>>
>>> 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 = ChOA.OriginalChOA(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="Chimp Optimization Algorithm", year=2020, difficulty="medium", 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
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# f decreases linearly from 2.5 to 0 over iterations, Eq. (3)
f = 2.5 - 2.5 * (epoch / self.epoch)
# Get the four best chimps (Attacker, Barrier, Chaser, Driver)
_, list_best, _ = self.get_special_agents(self.pop, n_best=4, minmax=self.problem.minmax)
x_attacker = list_best[0].solution
x_barrier = list_best[1].solution
x_chaser = list_best[2].solution
x_driver = list_best[3].solution
pop_new = []
for idx in range(self.pop_size):
# Attacker chimp position update
r1 = self.generator.random(self.problem.n_dims)
r2 = self.generator.random(self.problem.n_dims)
a1 = 2 * f * r1 - f
c1 = 2 * r2
d_attacker = np.abs(c1 * x_attacker - self.pop[idx].solution)
x1 = x_attacker - a1 * d_attacker
# Barrier chimp position update
r1 = self.generator.random(self.problem.n_dims)
r2 = self.generator.random(self.problem.n_dims)
a2 = 2 * f * r1 - f
c2 = 2 * r2
d_barrier = np.abs(c2 * x_barrier - self.pop[idx].solution)
x2 = x_barrier - a2 * d_barrier
# Chaser chimp position update
r1 = self.generator.random(self.problem.n_dims)
r2 = self.generator.random(self.problem.n_dims)
a3 = 2 * f * r1 - f
c3 = 2 * r2
d_chaser = np.abs(c3 * x_chaser - self.pop[idx].solution)
x3 = x_chaser - a3 * d_chaser
# Driver chimp position update
r1 = self.generator.random(self.problem.n_dims)
r2 = self.generator.random(self.problem.n_dims)
a4 = 2 * f * r1 - f
c4 = 2 * r2
d_driver = np.abs(c4 * x_driver - self.pop[idx].solution)
x4 = x_driver - a4 * d_driver
# Calculate new position as the average of all four leaders, Eq. (7)
pos_new = (x1 + x2 + x3 + x4) / 4.0
# Correct and check boundaries
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)