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Python演化计算基准函数详解

程序员文章站 2022-04-06 21:01:26
目录基准函数定义代码实现调用方法总结基准函数是测试演化计算算法性能的函数集,由于大部分基准函数集都是c/c++编写,python编写的基准函数比较少,因此本文实现了13个常用基准函数的python版。...

基准函数是测试演化计算算法性能的函数集,由于大部分基准函数集都是c/c++编写,python编写的基准函数比较少,因此本文实现了13个常用基准函数的python版。

基准函数定义

Python演化计算基准函数详解

代码实现

benchmark.py

import numpy as np
import copy
"""
author : robin_hua
update time : 2021.10.14
version : 1.0
"""
class sphere:
    def __init__(self, x):
        self.x = x
    def getvalue(self):
        res = np.sum(self.x**2)
        return res
class schwefel2_22:
    def __init__(self, x):
        self.x = x
    def getvalue(self):
        res = np.sum(np.abs(self.x)) + np.prod(np.abs(self.x))
        return res
class noise:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        d = self.x.shape[0]
        res = np.sum(np.arange(1, d + 1) * self.x ** 4) + np.random.random()
        return res
class schwefel2_21:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        res = np.max(np.abs(self.x))
        return res
class step:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        res = np.sum(int(self.x + 0.5) ** 2)
        return res
class rosenbrock:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        d = self.x.shape[0]
        res = np.sum(np.abs(100*(self.x[1:] - self.x[:-1]**2)**2 + (1 - self.x[:-1])**2))
        return res
class schwefel:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        d = self.x.shape[0]
        res = 418.9829*d - np.sum(self.x * np.sin(np.sqrt(np.abs(self.x))))
        return res
class rastrigin:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        d = self.x.shape[0]
        res = 10 * d + np.sum(self.x ** 2 - 10 * np.cos(2 * np.pi * self.x))
        return res
class ackley:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        d = self.x.shape[0]
        res = - 20 * np.exp(-0.2 * np.sqrt(np.mean(self.x ** 2)))
        res = res - np.exp(np.mean(np.cos(2 * np.pi * self.x))) + 20 + np.exp(1)
        return res
class griewank:
    def __init__(self,x):
        self.x = x
    def getvalue(self):
        d = self.x.shape[0]
        i = np.arange(1, d + 1)
        res = 1 + np.sum(self.x ** 2) / 4000 - np.prod(np.cos(self.x / np.sqrt(i)))
        return res
class generalized_penalized:
    def __init__(self,x):
        self.x = x
    def u(self,a,k,m):
        temp = copy.deepcopy(self.x)
        temp[-a <= temp.any() <= a] = 0
        temp[temp > a] = k*(temp[temp > a]-a)**m
        temp[temp < -a] = k * (-temp[temp < -a] - a) ** m
        """
        temp = np.zeros_like(self.x)
        d = self.x.shape[0]
        for i in range(d):
            if self.x[i]>a:
                temp[i] = k*(self.x[i]-a)**m
            elif self.x[i]<-a:
                temp[i] = k * (-self.x[i] - a) ** m
            else:
                pass
        """
        return temp
    def getvalue(self):
        d = self.x.shape[0]
        y = 1+1/4*(self.x+1)
        res = np.pi/d*(10*np.sin(np.pi*y[0])**2+np.sum((y[:-1]-1)**2*(1+10*np.sin(np.pi*y[1:])**2))+(y[-1]-1)**2)+np.sum(self.u(10,100,4))
        return res
def benchmark_func(x,func_num):
    func = func_list[func_num]
    res = func(x)
    return res
func_list = [sphere,schwefel2_22,noise,schwefel2_21,step,rosenbrock,schwefel,rastrigin,ackley,griewank,generalized_penalized]

调用方法

输入为向量x和函数编号func_num

import benchmark
import numpy as np
vector = np.random.random(30)
value = benchmark.benchmark_func(x=vector,func_num=0).getvalue()

总结

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