sedo 1.2.5

Social Entropy Diffusion Optimization Algorithm (SEDO)

社会熵扩散优化算法(SEDO) / Social Entropy Diffusion Optimizer (SEDO)

[English] | [中文] | [开发文档]

受社会学与量子物理启发的创新型全局优化算法

算法特点

• 🌐 文化空间信息传播模型
• ⚛️ 量子化粒子表示
• 🔥 基于熵流的探索-开发平衡
• 🎯 支持单目标/多目标优化
• ⚡ 并行计算加速

安装方法

pip install sedo

快速开始

不要使用 lambda 函数作为目标函数！

from sedo import SEDOptimizer

# 定义目标函数
def rastrigin(x):
    return 10*len(x) + sum(x**2 - 10*np.cos(2*np.pi*x))

# 初始化优化器
optimizer = SEDOptimizer(
    objective_func=rastrigin,
    problem_dim=20,
    n_particles=50
)

# 执行优化
optimizer.optimize(max_iter=200)

# 获取结果
best_solution = optimizer.get_best_solution()
print(f"最优解: {best_solution}")

对比测试结果

函数	算法	平均适应度	标准差
Sphere	SEDO	0.105087	0.057231
Sphere	PSO	0.097989	0.080966
Rosenbrock	SEDO	-25124.000000	531.337934
Rosenbrock	PSO	-24136.000000	1778.088862
Ackley	SEDO	1.739894	0.821921
Ackley	PSO	2.284575	0.898855

高级特性

文化传播可视化

from sedo.utils import plot_cultural_diffusion
plot_cultural_diffusion(optimizer.particles)

参数敏感性分析

from sedo.utils import parameter_sensitivity
results = parameter_sensitivity(
    objective_func,
    param_ranges={
        'barrier_height': (0.1, 1.0),
        'entropy_threshold': (0.3, 0.9)
    }
)

应用案例

1. 神经网络超参数优化

optimizer = SEDOptimizer(
    objective_func=neural_net_train,
    problem_dim=8,
    bounds=[(32,512), (0.0001,0.1), ...]  # 各参数范围
)

2. 投资组合优化

optimizer = SEDOptimizer(
    objective_func=portfolio_eval,
    problem_dim=10,
    multi_objective=True  # 同时优化收益和风险
)

参与贡献

欢迎提交Issue和Pull Request！

1. Fork项目
2. 创建特性分支 (git checkout -b feature/your-feature)
3. 提交更改 (git commit -am 'Add some feature')
4. 推送分支 (git push origin feature/your-feature)
5. 新建Pull Request

许可证

MIT License

Social Entropy Diffusion Optimizer (SEDO)

[English] | [中文] | [Development Documentation]

An innovative global optimization algorithm inspired by sociology and quantum physics

Key Features

• 🌐 Cultural space information diffusion model
• ⚛️ Quantum particle representation
• 🔥 Exploration-exploitation balance based on entropy flow
• 🎯 Single/Multi-objective optimization support
• ⚡ Parallel computing acceleration

Installation

pip install sedo

Quick Start

Do Not USE lambda function as objective function!

from sedo import SEDOptimizer

# Define objective function
def rastrigin(x):
    return 10*len(x) + sum(x**2 - 10*np.cos(2*np.pi*x))

# Initialize optimizer
optimizer = SEDOptimizer(
    objective_func=rastrigin,
    problem_dim=20,
    n_particles=50
)

# Run optimization
optimizer.optimize(max_iter=200)

# Get results
best_solution = optimizer.get_best_solution()
print(f"Optimal solution: {best_solution}")

Comparative Test Results

Function	Algorithm	Avg Fitness	Std Dev
Sphere	SEDO	0.105087	0.057231
Sphere	PSO	0.097989	0.080966
Rosenbrock	SEDO	-25124.000000	531.337934
Rosenbrock	PSO	-24136.000000	1778.088862
Ackley	SEDO	1.739894	0.821921
Ackley	PSO	2.284575	0.898855

Advanced Features

Cultural Diffusion Visualization

from sedo.utils import plot_cultural_diffusion
plot_cultural_diffusion(optimizer.particles)

Parameter Sensitivity Analysis

from sedo.utils import parameter_sensitivity
results = parameter_sensitivity(
    objective_func,
    param_ranges={
        'barrier_height': (0.1, 1.0),
        'entropy_threshold': (0.3, 0.9)
    }
)

Application Examples

1. Neural Network Hyperparameter Tuning

optimizer = SEDOptimizer(
    objective_func=neural_net_train,
    problem_dim=8,
    bounds=[(32,512), (0.0001,0.1), ...]  # Parameter ranges
)

2. Portfolio Optimization

optimizer = SEDOptimizer(
    objective_func=portfolio_eval,
    problem_dim=10,
    multi_objective=True  # Optimize return and risk simultaneously
)

How to Contribute

We welcome issues and pull requests!

1. Fork the project
2. Create your feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Create a new Pull Request

License

MIT License

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