MEALPY: 最先进的元启发式算法Python库

MEALPY简介

MEALPY (MEta-heuristic ALgorithms in PYthon) 是一个开源的Python库,包含了大量最先进的元启发式算法。这些算法属于基于种群的算法(PMA),是近似优化领域中最流行的算法。

MEALPY的主要特点包括:

免费开源:采用GNU通用公共许可证(GPL) V3许可
算法丰富:共有215种算法,包括190种官方算法(原始、混合、变体)和25种开发算法
文档完善:提供详细的在线文档
兼容性强:支持Python 3.7及以上版本
依赖简单:仅依赖numpy、scipy、pandas和matplotlib

MEALPY分类

MEALPY的目标

MEALPY的主要目标是:

免费分享元启发式领域的知识
帮助各领域的研究人员快速访问优化算法
实现经典和最新的元启发式算法,涵盖元启发式的整个历史

MEALPY的应用

使用MEALPY,您可以:

分析元启发式算法的参数
对算法进行定性和定量分析
分析算法的收敛速度
测试和分析算法的可扩展性和稳健性
以多种格式(csv、json、pickle、png、pdf、jpeg)保存结果
导出和导入模型
解决各种优化问题

安装和使用

可以通过pip安装MEALPY的稳定版本:

pip install mealpy==3.0.1

安装完成后,可以像导入其他Python模块一样导入MEALPY:

import mealpy
print(mealpy.__version__)
print(mealpy.get_all_optimizers())

示例

简单的基准函数优化

from mealpy import FloatVar, SMA
import numpy as np

def objective_function(solution):
    return np.sum(solution**2)

problem = {
    "obj_func": objective_function,
    "bounds": FloatVar(lb=(-100.,)*30, ub=(100.,)*30),
    "minmax": "min",
    "log_to": None,
}

model = SMA.OriginalSMA(epoch=100, pop_size=50, pr=0.03)
g_best = model.solve(problem)
print(f"Best solution: {g_best.solution}, Best fitness: {g_best.target.fitness}")

大规模优化

from mealpy import FloatVar, SHADE
import numpy as np

def objective_function(solution):
    return np.sum(solution**2)

problem = {
    "obj_func": objective_function,
    "bounds": FloatVar(lb=(-1000.,)*10000, ub=(1000.,)*10000),  # 10000维
    "minmax": "min",
    "log_to": "console",
}

optimizer = SHADE.OriginalSHADE(epoch=10000, pop_size=100)
g_best = optimizer.solve(problem)
print(f"Best solution: {g_best.solution}, Best fitness: {g_best.target.fitness}")

分布式/并行优化

MEALPY支持使用多线程或多进程进行分布式优化:

from mealpy import FloatVar, SMA
import numpy as np

def objective_function(solution):
    return np.sum(solution**2)

problem = {
    "obj_func": objective_function,
    "bounds": FloatVar(lb=(-100.,)*100, ub=(100.,)*100),
    "minmax": "min",
    "log_to": "console",
}

optimizer = SMA.OriginalSMA(epoch=10000, pop_size=100, pr=0.03)

# 使用10个线程
optimizer.solve(problem, mode="thread", n_workers=10)
print(f"Best solution: {optimizer.g_best.solution}, Best fitness: {optimizer.g_best.target.fitness}")

# 使用8个CPU核心
optimizer.solve(problem, mode="process", n_workers=8)
print(f"Best solution: {optimizer.g_best.solution}, Best fitness: {optimizer.g_best.target.fitness}")

自定义问题

MEALPY允许用户定义自定义的优化问题。以下是一个优化SVM超参数的例子:

from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import datasets, metrics

from mealpy import FloatVar, StringVar, IntegerVar, BoolVar, MixedSetVar, SMA, Problem

# 加载数据集
X, y = datasets.load_breast_cancer(return_X_y=True)

# 创建训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)

sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.transform(X_test)

data = {
    "X_train": X_train_std,
    "X_test": X_test_std,
    "y_train": y_train,
    "y_test": y_test
}

class SvmOptimizedProblem(Problem):
    def __init__(self, bounds=None, minmax="max", data=None, **kwargs):
        self.data = data
        super().__init__(bounds, minmax, **kwargs)

    def obj_func(self, x):
        x_decoded = self.decode_solution(x)
        C_paras, kernel_paras = x_decoded["C_paras"], x_decoded["kernel_paras"]
        degree, gamma, probability = x_decoded["degree_pras"], x_decoded["gamma_paras"], x_decoded["probability_paras"]

        svc = SVC(C=C_paras, kernel=kernel_paras, degree=degree, 
                  gamma=gamma, probability=probability, random_state=1)
        # 拟合模型
        svc.fit(self.data["X_train"], self.data["y_train"])
        # 进行预测
        y_predict = svc.predict(self.data["X_test"])
        # 评估性能
        return metrics.accuracy_score(self.data["y_test"], y_predict)

my_bounds = [
    FloatVar(lb=0.01, ub=1000., name="C_paras"),
    StringVar(valid_sets=('linear', 'poly', 'rbf', 'sigmoid'), name="kernel_paras"),
    IntegerVar(lb=1, ub=5, name="degree_paras"),
    MixedSetVar(valid_sets=('scale', 'auto', 0.01, 0.05, 0.1, 0.5, 1.0), name="gamma_paras"),
    BoolVar(n_vars=1, name="probability_paras"),
]
problem = SvmOptimizedProblem(bounds=my_bounds, minmax="max", data=data)
model = SMA.OriginalSMA(epoch=100, pop_size=20)
model.solve(problem)

print(f"Best agent: {model.g_best}")
print(f"Best solution: {model.g_best.solution}")
print(f"Best accuracy: {model.g_best.target.fitness}")
print(f"Best parameters: {model.problem.decode_solution(model.g_best.solution)}")