参考文献:
K. Tang, P. Yang and X. Yao, "Negatively Correlated Search," in IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 542-550, March 2016, doi: 10.1109/JSAC.2016.2525458.
算法包sustech_ncs下,NCS
是NCS-C的多进程并行加速的实现,NCS_noMul
是未进行多进程加速的版本,NCS_asym
是加入了非对称优化的实现版本。
使用示例:
import numpy as np
# import optproblems.cec2005 as benchmark
import opfunu.cec_based.cec2005 as benchmark
from sustech_ncs import NCS
if __name__=='__main__':
dimension0=30
pop_size0=10 #
sigma0=0.2 # 注意sigma不能是整数
r0=0.80 #
epoch0=10 #
T0=30000
scope = np.array([[-np.pi, np.pi]] * dimension0)
# 选择测试使用的fitness function
function=benchmark.F122005(ndim=dimension0).evaluate # opfunu
# function=benchmark.F1(num_variables=dimension0) # optproblems
optimizer = NCS(function, dimensions=dimension0, pop_size=pop_size0, sigma=np.full(pop_size0, sigma0), r=r0, epoch=epoch0, T_max=T0 ,scope=scope)
best_solution, best_f_solution = optimizer.NCS_run()
print("Best solution found by NCS:", best_solution)
print("Objective function value:", best_f_solution)
print(f"Function: {optimizer.objective_function_individual}, \n"
f"Dimensions: {optimizer.dimensions}, Population Size: {optimizer.pop_size}, Sigma: {sigma0}, \n"
f"R: {optimizer.r}, Epoch: {optimizer.epoch}, T_max: {optimizer.T_max}, "
f"Scope: {optimizer.scope[0]}")
# # opfunu用法
# dimension = 10
# problem = benchmark.F12005(dimension)
# solution = [0.1] * dimension
# value = problem.evaluate(solution)
# print(value)
# # optproblems用法
# dimension = 10
# problem = benchmark.F1(dimension)
# solution = [0.1] * dimension
# value = problem(solution)