#!/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 OriginalWHO(Optimizer):
"""
The original version of: Wildebeest Herd Optimization (WHO)
Links:
1. https://doi.org/10.3233/JIFS-190495
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ n_explore_step (int): [2, 10] -> better [2, 4], number of exploration step
+ n_exploit_step (int): [2, 10] -> better [2, 4], number of exploitation step
+ eta (float): (0, 1.0) -> better [0.05, 0.5], learning rate
+ p_hi (float): (0, 1.0) -> better [0.7, 0.95], the probability of wildebeest move to another position based on herd instinct
+ local_alpha (float): (0, 3.0) -> better [0.5, 0.9], control local movement (alpha 1)
+ local_beta (float): (0, 3.0) -> better [0.1, 0.5], control local movement (beta 1)
+ global_alpha (float): (0, 3.0) -> better [0.1, 0.5], control global movement (alpha 2)
+ global_beta (float): (0, 3.0), control global movement (beta 2)
+ delta_w (float): (0.5, 5.0) -> better [1.0, 2.0], dist to worst
+ delta_c (float): (0.5, 5.0) -> better [1.0, 2.0], dist to best
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, WHO
>>>
>>> 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 = WHO.OriginalWHO(epoch=1000, pop_size=50, n_explore_step = 3, n_exploit_step = 3, eta = 0.15, p_hi = 0.9,
>>> local_alpha=0.9, local_beta=0.3, global_alpha=0.2, global_beta=0.8, delta_w=2.0, delta_c=2.0)
>>> 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] Amali, D. and Dinakaran, M., 2019. Wildebeest herd optimization: a new global optimization algorithm inspired
by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, 37(6), pp.8063-8076.
"""
def __init__(self, epoch=10000, pop_size=100, n_explore_step=3, n_exploit_step=3, eta=0.15, p_hi=0.9,
local_alpha=0.9, local_beta=0.3, global_alpha=0.2, global_beta=0.8, delta_w=2.0, delta_c=2.0, **kwargs):
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
n_explore_step (int): default = 3, number of exploration step
n_exploit_step (int): default = 3, number of exploitation step
eta (float): default = 0.15, learning rate
p_hi (float): default = 0.9, the probability of wildebeest move to another position based on herd instinct
local_alpha (float): control local movement (alpha 1)
local_beta (float): control local movement (beta 1)
global_alpha (float): control global movement (alpha 2)
global_beta (float): control global movement (beta 2)
delta_w (float): dist to worst
delta_c (float): dist to best
"""
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.n_explore_step = self.validator.check_int("n_explore_step", n_explore_step, [2, 10])
self.n_exploit_step = self.validator.check_int("n_exploit_step", n_exploit_step, [2, 10])
self.eta = self.validator.check_float("eta", eta, (0, 1.0))
self.p_hi = self.validator.check_float("p_hi", p_hi, (0, 1.0))
self.local_alpha = self.validator.check_float("local_alpha", local_alpha, (0, 3.0))
self.local_beta = self.validator.check_float("local_beta", local_beta, (0, 3.0))
self.global_alpha = self.validator.check_float("global_alpha", global_alpha, (0, 3.0))
self.global_beta = self.validator.check_float("global_beta", global_beta, (0, 3.0))
self.delta_w = self.validator.check_float("delta_w", delta_w, (0.5, 5.0))
self.delta_c = self.validator.check_float("delta_c", delta_c, (0.5, 5.0))
self.set_parameters(["epoch", "pop_size", "n_explore_step", "n_exploit_step",
"eta", "p_hi", "local_alpha", "local_beta", "global_alpha", "global_beta", "delta_w", "delta_c"])
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
"""
## Begin the Wildebeest Herd Optimization process
pop_new = []
for idx in range(0, self.pop_size):
### 1. Local movement (Milling behaviour)
local_list = []
for j in range(0, self.n_explore_step):
temp = self.pop[idx].solution + self.eta * self.generator.uniform() * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(temp)
agent = self.generate_empty_agent(pos_new)
local_list.append(agent)
if self.mode not in self.AVAILABLE_MODES:
local_list[-1].target = self.get_target(pos_new)
local_list = self.update_target_for_population(local_list)
best_local = self.get_best_agent(local_list, self.problem.minmax)
temp = self.local_alpha * best_local.solution + self.local_beta * (self.pop[idx].solution - best_local.solution)
pos_new = self.correct_solution(temp)
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)
for idx in range(0, self.pop_size):
### 2. Herd instinct
idr = self.generator.choice(range(0, self.pop_size))
if self.compare_target(self.pop[idr].target, self.pop[idx].target, self.problem.minmax) and self.generator.random() < self.p_hi:
temp = self.global_alpha * self.pop[idx].solution + self.global_beta * self.pop[idr].solution
pos_new = self.correct_solution(temp)
tar_new = self.get_target(pos_new)
if self.compare_target(tar_new, self.pop[idx].target, self.problem.minmax):
self.pop[idx].update(solution=pos_new, target=tar_new)
_, best, worst = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
g_best, g_worst = best[0], worst[0]
pop_child = []
for idx in range(0, self.pop_size):
dist_to_worst = np.linalg.norm(self.pop[idx].solution - g_worst.solution)
dist_to_best = np.linalg.norm(self.pop[idx].solution - g_best.solution)
### 3. Starvation avoidance
if dist_to_worst < self.delta_w:
temp = self.pop[idx].solution + self.generator.uniform() * (self.problem.ub - self.problem.lb) * \
self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(temp)
agent = self.generate_empty_agent(pos_new)
pop_child.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)
### 4. Population pressure
if 1.0 < dist_to_best and dist_to_best < self.delta_c:
temp = g_best.solution + self.eta * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(temp)
agent = self.generate_empty_agent(pos_new)
pop_child.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)
### 5. Herd social memory
for jdx in range(0, self.n_exploit_step):
temp = g_best.solution + 0.1 * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(temp)
agent = self.generate_empty_agent(temp)
pop_child.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_child = self.update_target_for_population(pop_child)
pop_child = self.get_sorted_and_trimmed_population(pop_child, self.pop_size, self.problem.minmax)
self.pop = self.greedy_selection_population(self.pop, pop_child, self.problem.minmax)