🧠 Intelligent Optimization Algorithm Study

A beginner-friendly repository for learning intelligent optimization algorithms, featuring 3 algorithms benchmarked on 10 test functions.

📁 Directory Structure

├── algorithm/              # Algorithm implementations
│   ├── pso.py              #   Particle Swarm Optimization
│   ├── ga.py               #   Genetic Algorithm
│   └── ssa.py              #   Sparrow Search Algorithm
├── cec2017/                # Benchmark functions (10)
│   ├── __init__.py
│   └── functions.py        #   Sphere, BentCigar, Zakharov,
│                           #   Rosenbrock, Rastrigin, Ackley,
│                           #   Griewank, Schwefel, Levy,
│                           #   ExpandedScafferF6
├── main.py                 # Entry point: batch run & comparison
├── plot.py                 # Convergence & summary plots
├── results/                # Output images + CSV
└── README.md

🚀 Quick Start

# Install dependencies
pip install numpy matplotlib

# Run all experiments
python main.py

After running, results/ will contain:

• comparison_*.png — per-function comparison of all 3 algorithms (mean ± std)
• summary_all.png — full grid summary across all functions
• summary.csv — final best-fitness mean & std for each combination

🔧 Customization

Edit the constants in main.py:

DIM = 30          # Problem dimension
POP_SIZE = 50     # Population size
MAX_ITER = 200    # Max iterations
N_SEEDS = 5       # Random seeds (for error bands)

📊 Algorithms

Algorithm	Tag	Family	Core Idea
Particle Swarm Optimization	PSO	Swarm Intelligence	Particles adjust velocity via personal & global best
Genetic Algorithm	GA	Evolutionary	Selection, crossover, mutation + elitism
Sparrow Search Algorithm	SSA	Swarm Intelligence	Producer-scrounger-vigilante cooperative foraging

📈 Benchmark Functions

10 functions covering unimodal/multimodal and separable/non-separable landscapes. All have a global minimum of 0.

📝 License

MIT