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SSA2017.py
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SSA2017.py
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# ！usr/bin/env python
# -*- coding: utf-8 -*-
# Time : 2021/12/5 15:39
# @Author : LucXiong
# @Project : Model
# @File : SSA2017.py
############################################################################
# Ref: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems.pdf
############################################################################
# Required Libraries
import numpy as np
import math
import random
import os
import matplotlib.pyplot as plt
import test_function
class salp_swarm_algorithm():
def __init__(self, pop_size=50, n_dim=2, max_iter=150, lb=[-5,-5], ub=[5,5], func=None):
self.pop = pop_size
self.lb = lb
self.ub = ub
self.func = func
self.n_dim = n_dim
self.max_iter = max_iter
self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.pop, self.n_dim))
self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # y = f(x) for all particles
self.pbest_x = self.X.copy() # personal best location of every particle in history
self.pbest_y = [np.inf for i in range(self.pop)] # best image of every particle in history
self.gbest_x = self.pbest_x.mean(axis=0).reshape(1, -1) # global best location for all particles
self.gbest_y = np.inf # global best y for all particles
self.gbest_y_hist = [] # gbest_y of every iteration
self.update_pbest()
self.update_gbest()
def update_pbest(self):
'''
personal best
:return:
'''
for i in range(len(self.Y)):
if self.pbest_y[i] > self.Y[i]:
self.pbest_x[i] = self.X[i]
self.pbest_y[i] = self.Y[i]
def update_gbest(self):
'''
global best
:return:
'''
idx_min = self.pbest_y.index(min(self.pbest_y))
if self.gbest_y > self.pbest_y[idx_min]:
self.gbest_x = self.X[idx_min, :].copy()
self.gbest_y = self.pbest_y[idx_min]
# Function: Updtade Position
def update_position(self, c1):
for i in range(0, self.pop):
if (i <= self.pop / 2): # 领导者比例
for j in range(0, self.n_dim):
c2 = int.from_bytes(os.urandom(8), byteorder="big") / ((1 << 64) - 1)
c3 = int.from_bytes(os.urandom(8), byteorder="big") / ((1 << 64) - 1)
if (c3 >= 0.5): # c3 < 0.5
try:
self.X[i, j] = np.clip((self.gbest_x[0][j] + c1 * ((self.ub[j] - self.lb[j]) * c2 + self.lb[j])), self.lb[j], self.ub[j])
except:
self.X[i, j] = np.clip((self.gbest_x[j] + c1 * ((self.ub[j] - self.lb[j]) * c2 + self.lb[j])), self.lb[j], self.ub[j])
else:
try:
self.X[i, j] = np.clip((self.gbest_x[0][j] - c1 * ((self.ub[j] - self.lb[j]) * c2 + self.lb[j])), self.lb[j], self.ub[j])
except:
self.X[i, j] = np.clip((self.gbest_x[j] - c1 * ((self.ub[j] - self.lb[j]) * c2 + self.lb[j])), self.lb[j], self.ub[j])
else: # 追随者比例
for j in range(0, self.n_dim):
self.X[i, j] = np.clip(((self.X[i - 1, j] + self.X[i, j]) / 2), self.lb[j], self.ub[j])
self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # y = f(x) for all particles
def run(self):
for i in range(self.max_iter):
c1 = 2 * math.exp(-(4 * ((i+1) / self.max_iter)) ** 2)
self.update_position(c1)
self.update_pbest()
self.update_gbest()
self.gbest_y_hist.append(self.gbest_y)
self.best_x, self.best_y = self.gbest_x, self.gbest_y
return self.best_x, self.best_y
if __name__ == '__main__':
n_dim = 30
lb = [-5 for i in range(n_dim)]
ub = [5 for i in range(n_dim)]
demo_func = test_function.fu1
ssa = salp_swarm_algorithm(pop_size=50, n_dim=n_dim, max_iter=150, lb=lb, ub=ub, func=demo_func)
ssa.run()
print('best_x is ', ssa.gbest_x, 'best_y is', ssa.gbest_y)
print(f'{demo_func(ssa.gbest_x)}\t{ssa.gbest_x}')
plt.plot(ssa.gbest_y_hist)
plt.show()