#!/usr/bin/env python
# Created by "Thieu" at 17:28, 21/05/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalMPA(Optimizer):
"""
The developed version: Marine Predators Algorithm (MPA)
Links:
1. https://www.sciencedirect.com/science/article/abs/pii/S0957417420302025
2. https://www.mathworks.com/matlabcentral/fileexchange/74578-marine-predators-algorithm-mpa
Notes:
1. To use the original paper, set the training mode = "swarm"
2. They update the whole population at the same time before update the fitness
3. Two variables that they consider it as constants which are FADS = 0.2 and P = 0.5
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, MPA
>>>
>>> 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 = MPA.OriginalMPA(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] Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020).
Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377.
"""
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 initialize_variables(self):
self.FADS = 0.2
self.P = 0.5
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
CF = (1 - epoch/self.epoch)**(2 * 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)
RL = self.get_levy_flight_step(beta=1.5, multiplier=0.05, size=(self.pop_size, self.problem.n_dims), case=-1)
RB = self.generator.standard_normal((self.pop_size, self.problem.n_dims))
per1 = self.generator.permutation(self.pop_size)
per2 = self.generator.permutation(self.pop_size)
pop_new = []
for idx in range(0, self.pop_size):
R = self.generator.random(self.problem.n_dims)
t = self.epoch
if t < self.epoch / 3: # Phase 1 (Eq.12)
step_size = RB[idx] * (self.g_best.solution - RB[idx] * self.pop[idx].solution)
pos_new = self.pop[idx].solution + self.P * R * step_size
elif self.epoch / 3 < t < 2*self.epoch / 3: # Phase 2 (Eqs. 13 & 14)
if idx > self.pop_size / 2:
step_size = RB[idx] * (RB[idx] * self.g_best.solution - self.pop[idx].solution)
pos_new = self.g_best.solution + self.P * CF * step_size
else:
step_size = RL[idx] * (self.g_best.solution - RL[idx] * self.pop[idx].solution)
pos_new = self.pop[idx].solution + self.P * R * step_size
else: # Phase 3 (Eq. 15)
step_size = RL[idx] * (RL[idx] * self.g_best.solution - self.pop[idx].solution)
pos_new = self.g_best.solution + self.P * CF * step_size
pos_new = self.correct_solution(pos_new)
if self.generator.random() < self.FADS:
u = np.where(self.generator.random(self.problem.n_dims) < self.FADS, 1, 0)
pos_new = pos_new + CF * (self.problem.lb + self.generator.random(self.problem.n_dims) * (self.problem.ub - self.problem.lb)) * u
else:
r = self.generator.random()
step_size = (self.FADS * (1 - r) + r) * (self.pop[per1[idx]].solution - self.pop[per2[idx]].solution)
pos_new = pos_new + step_size
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(self.pop[idx], agent, 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)