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https://github.com/nickory/cpo-python

The Crested Porcupine Optimizer (CPO) is a novel metaheuristic algorithm inspired by the defensive mechanisms of the crested porcupine.
https://github.com/nickory/cpo-python

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The Crested Porcupine Optimizer (CPO) is a novel metaheuristic algorithm inspired by the defensive mechanisms of the crested porcupine.

Host: GitHub
URL: https://github.com/nickory/cpo-python
Owner: Nickory
Created: 2024-11-10T10:58:06.000Z (2 months ago)
Default Branch: main
Last Pushed: 2024-12-07T09:58:52.000Z (about 1 month ago)
Last Synced: 2024-12-07T10:26:38.290Z (about 1 month ago)
Language: Python
Size: 14.4 MB
Stars: 2
Watchers: 1
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# Crested Porcupine Optimizer (CPO) in Python

### Overview

This project provides a **Python implementation and replication** of the **Crested Porcupine Optimizer (CPO)**, a nature-inspired metaheuristic algorithm originally proposed in the paper *"Crested Porcupine Optimizer: A New Nature-Inspired Metaheuristic"* by Mohamed Abdel-Basset et al. Our implementation aims to faithfully reproduce the algorithm's behaviors and validate its effectiveness through various tests, including **benchmark functions** and **path planning** applications.

### Features

- **CPO Algorithm Replication**: A complete Python replication of the original CPO algorithm, with configurable parameters for various optimization tasks.
- **Benchmark Testing on CEC Functions**: Validated on CEC benchmark functions (CEC2017, CEC2020, etc.), comparing the algorithm’s performance and robustness.
- **A-Enhanced Path Planning with CPO**: Applies CPO to enhance traditional A* path planning, balancing exploration and exploitation for optimized paths in complex environments.
- **Randomized Obstacle Map Generation**: Generates random obstacle maps to test path planning performance in varied scenarios.
- **Multi-Objective Optimization**: Extends CPO for multi-objective optimization, supporting applications that require Pareto-optimal front analysis.

### Repository Structure

#### 1. **Path Planning Implementations**
- **`A*-CPO_path_2d.py`**: Implements 2D path planning by combining the Crested Porcupine Optimizer (CPO) with the A* algorithm for hybrid route planning.
- **`A*-CPO_path_3d.py`**: Extends the 2D path planning approach to 3D environments using the A*-CPO hybrid method.
- **`A*-CPO_path_3d_v2.py`**: An improved version of the 3D path planning script with optimizations for enhanced performance.

#### 2. **Benchmarking and Testing**
- **`CPO_cec2017.py`**: Contains benchmark functions from the CEC 2017 suite, supporting the testing of CPO algorithm performance.
- **`CPO_cec2022.py`**: Similar to `CPO_cec2017.py`, but using benchmark functions from the CEC 2022 suite.
- **`CPO_convergenceCurve.py`**: Visualizes convergence curves to assess the performance and stability of various CPO algorithm versions.

#### 3. **Multi-Objective Optimization**
- **`CPO_multi_v3.py`**: Implements multi-objective optimization using CPO, with support for Pareto front visualization.
- **`CPO_multi_v4.py`**: Extends the multi-objective optimization capabilities of CPO, adding support for three objectives.

#### 4. **Population Dynamics and Core Algorithm**
- **`CPO_population.py`**: Manages population dynamics within the CPO framework, supporting algorithm functionality.
- **`CPO_algorithm_v4_class.py`**: Implements the object-oriented version of the Crested Porcupine Optimizer (CPO) algorithm using classes, encapsulating the core functionalities, including fitness functions, particles, and the optimizer itself.

### 5. **Other Files**
- **`expriment_report.pdf`**: A report documenting experimental results, performance comparisons, and analyses of the original CPO algorithm.
- **`CPO_OOP_report.pdf`**: A report discussing the object-oriented version of the CPO algorithm, explaining the application of classes, private members, protected members, inheritance, and polymorphism, and how these features improve maintainability and scalability of the algorithm.
- **`README.md`**: Documentation and overview of the repository, providing an introduction to the CPO algorithm, its various implementations, and instructions on usage.

### Key Concepts

- **Defensive Mechanisms of CPO**: Each phase of CPO mirrors a defensive behavior observed in porcupines:
- *Sight*: Broad random exploration.
- *Sound*: Social interaction leading to further exploration.
- *Odor*: Convergence towards promising solutions with diversity retention.
- *Physical Attack*: Final precision adjustments around optimal solutions.
- **Cyclic Population Reduction**: The population size decreases over time, focusing on promising solutions, replicating the porcupine’s response under threat.

### Getting Started

1. **Dependencies**: Install required libraries.
```bash
pip install numpy matplotlib

2. **Run a Benchmark Test**:

```bash
python CPO_cec.py
```

3. **Path Planning Example**:

```bash
python CPO_path_planning.py
```

### Example Results

This repository includes sample results comparing the CPO algorithm with other popular optimizers on benchmark functions such as Sphere, Rosenbrock, and Schwefel functions, showcasing CPO's balance between exploration and convergence. For path planning, CPO demonstrates potential in generating non-overlapping, direct paths and avoiding local minima.

### Acknowledgment

This repository is a replication and extension of the CPO algorithm based on the original paper:

- **Original Paper**: "Crested Porcupine Optimizer: A New Nature-Inspired Metaheuristic" by Mohamed Abdel-Basset et al., published in *Knowledge-Based Systems*.

We would also like to express our sincere thanks to **Dr.Chengzhi Qu** from **Nanjing university of information science and technoledge**, for his valuable guidance and for providing the topic for this project.

### Additional Documentation

- **NUIST Experiment Report**: Contains detailed documentation of our experimental setup, results, and analysis of CPO on various benchmark functions and path planning tasks.

### CEC2017 Benchmark Function Visualization

The following figures showcase the performance of the Crested Porcupine Optimizer (CPO) on various CEC2017 benchmark functions (F1-F6). Each function includes a surface plot, convergence curve, and performance comparison with other optimizers.

### Additional Analysis

These visualizations demonstrate the Crested Porcupine Optimizer’s ability to handle various optimization landscapes, showing convergence speed and accuracy across different types of functions. Each function presents unique challenges, and CPO's performance on these benchmarks highlights its robustness and adaptability.