Heuristic Optimization Algorithms - A Code-First Learning Series

Heuristic optimization is not one algorithm. It is a family of search strategies with different assumptions, operators, strengths, and failure modes. This series therefore gives each algorithm its own article and its own readable Python implementation instead of compressing everything into a single survey.

All code lives in the OptimizationAlgorithm repository. The implementations share explicit bounds, minimization semantics, seeded NumPy random generators, result objects, and automated tests, so the algorithms can be studied and compared without unrelated interface differences.

Series map

#	Algorithm	Central idea	Article	Implementation
1	Particle Swarm Optimization (PSO)	Learn from personal and swarm experience	Read the PSO article	pso.py
2	Genetic Algorithm (GA)	Evolve a population through selection and variation	Read the GA article	ga.py
3	Differential Evolution (DE)	Use population differences as search directions	Read the DE article	de.py
4	Simulated Annealing (SA)	Escape local optima by accepting controlled uphill moves	Read the SA article	sa.py
5	NSGA-II	Preserve a diverse set of Pareto-optimal trade-offs	Read the NSGA-II article	nsga2.py

A shared experimental interface

The four single-objective algorithms accept the same objective and bounds:

from optimization_algorithms import ParticleSwarmOptimizer
from optimization_algorithms.benchmarks import rastrigin

optimizer = ParticleSwarmOptimizer(seed=42)
result = optimizer.minimize(rastrigin, [(-5.12, 5.12)] * 5)

print(result.x)
print(result.fun)
print(result.history)

Replace ParticleSwarmOptimizer with GeneticAlgorithm, DifferentialEvolution, or SimulatedAnnealing to keep the problem fixed while changing the search strategy. NSGA-II uses the same bounds convention but returns decision vectors and objective vectors for a non-dominated set.

What each article contains

Each article follows the same structure:

1. the intuition behind the algorithm;
2. the update rules or genetic operators;
3. a guided reading of the repository implementation;
4. a runnable benchmark example;
5. parameter choices, common failure modes, and suitable use cases.

The goal is not to declare a universal winner. It is to make the behavior of each method understandable enough that an optimizer can be chosen, tuned, and debugged deliberately.

Future additions will extend this index with CMA-ES, MOPSO, MOEA/D, constraint handling, and mixed discrete-continuous optimization.