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AFSA.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time : 2024/12
# @Author : github.com/nafiuny
import numpy as np
from scipy import spatial
class AFSA:
def __init__(self, func, n_dim, size_pop=50, max_iter=300,
max_try_num=100, step=0.5, visual=0.3,
q=0.98, delta=0.5):
self.func = func
self.n_dim = n_dim
self.size_pop = size_pop
self.max_iter = max_iter
self.max_try_num = max_try_num
self.step = step
self.visual = visual
self.q = q
self.delta = delta
self.X = np.random.rand(self.size_pop, self.n_dim)
self.Y = np.array([self.func(x) for x in self.X])
best_idx = self.Y.argmin()
self.best_x, self.best_y = self.X[best_idx, :], self.Y[best_idx]
self.best_X, self.best_Y = self.best_x, self.best_y # will be deprecated, use lowercase
self.history_positions = []
def move_to_target(self, idx_individual, x_target):
'''
move to target
called by prey(), swarm(), follow()
:param idx_individual:
:param x_target:
:return:
'''
x = self.X[idx_individual, :]
x_new = x + self.step * np.random.rand() * (x_target - x)
# x_new = x_target
self.X[idx_individual, :] = x_new
self.Y[idx_individual] = self.func(x_new)
if self.Y[idx_individual] < self.best_Y:
self.best_x = self.X[idx_individual, :].copy()
self.best_y = self.Y[idx_individual].copy()
def move(self, idx_individual):
'''
randomly move to a point
:param idx_individual:
:return:
'''
r = 2 * np.random.rand(self.n_dim) - 1
x_new = self.X[idx_individual, :] + self.visual * r
self.X[idx_individual, :] = x_new
self.Y[idx_individual] = self.func(x_new)
if self.Y[idx_individual] < self.best_Y:
self.best_X = self.X[idx_individual, :].copy()
self.best_Y = self.Y[idx_individual].copy()
def prey(self, idx_individual):
'''
prey
:param idx_individual:
:return:
'''
for try_num in range(self.max_try_num):
r = 2 * np.random.rand(self.n_dim) - 1
x_target = self.X[idx_individual, :] + self.visual * r
if self.func(x_target) < self.Y[idx_individual]: # 捕食成功
self.move_to_target(idx_individual, x_target)
return None
self.move(idx_individual)
def find_individual_in_vision(self, idx_individual):
distances = spatial.distance.cdist(self.X[[idx_individual], :], self.X, metric='euclidean').reshape(-1)
idx_individual_in_vision = np.argwhere((distances > 0) & (distances < self.visual))[:, 0]
return idx_individual_in_vision
def swarm(self, idx_individual):
idx_individual_in_vision = self.find_individual_in_vision(idx_individual)
num_idx_individual_in_vision = len(idx_individual_in_vision)
if num_idx_individual_in_vision > 0:
individual_in_vision = self.X[idx_individual_in_vision, :]
center_individual_in_vision = individual_in_vision.mean(axis=0)
center_y_in_vision = self.func(center_individual_in_vision)
if center_y_in_vision * num_idx_individual_in_vision < self.delta * self.Y[idx_individual]:
self.move_to_target(idx_individual, center_individual_in_vision)
return None
self.prey(idx_individual)
def follow(self, idx_individual):
idx_individual_in_vision = self.find_individual_in_vision(idx_individual)
num_idx_individual_in_vision = len(idx_individual_in_vision)
if num_idx_individual_in_vision > 0:
individual_in_vision = self.X[idx_individual_in_vision, :]
y_in_vision = np.array([self.func(x) for x in individual_in_vision])
idx_target = y_in_vision.argmin()
x_target = individual_in_vision[idx_target]
y_target = y_in_vision[idx_target]
if y_target * num_idx_individual_in_vision < self.delta * self.Y[idx_individual]:
self.move_to_target(idx_individual, x_target)
return None
self.prey(idx_individual)
def run(self, max_iter=None):
self.max_iter = max_iter or self.max_iter
for epoch in range(self.max_iter):
if epoch == 0 or epoch == self.max_iter - 1:
self.history_positions.append(self.X.copy())
for idx_individual in range(self.size_pop):
self.swarm(idx_individual)
self.follow(idx_individual)
self.visual *= self.q
# self.best_X, self.best_Y = self.best_x, self.best_y # will be deprecated, use lowercase
return self.best_x, self.best_y