CUSTOMHyS Documentation

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Customising Optimisation Metaheuristics via Hyper-heuristic Search (CUSTOMHyS) is a Python framework that provides tools for solving continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics.

The framework employs a strategy based on Simulated Annealing to search the heuristic space and build tailored metaheuristics. Several search operators extracted from ten well-known metaheuristics serve as building blocks for assembling new optimisers.

Note

Detailed information about the theoretical background can be found in the References section.

Quick Example

from customhys import benchmark_func as bf
from customhys.metaheuristic import Metaheuristic

# Define a problem
problem = bf.Sphere(2)
prob = {
    "function": problem.get_func_val,
    "is_constrained": True,
    "boundaries": problem.get_search_range(),
}

# Define search operators
search_operators = [
    ("random_search", {"scale": 0.01, "distribution": "uniform"}, "greedy"),
    ("swarm_dynamic", {"self_conf": 2.54, "swarm_conf": 2.56, "version": "inertial", "inertial_weight": 0.7}, "all"),
]

# Create and run the metaheuristic
mh = Metaheuristic(prob, search_operators, num_agents=30, num_iterations=100)
mh.run()
position, fitness = mh.get_solution()
print(f"Best fitness: {fitness:.6f}")

Getting Started

User Guide

API Reference

Project

Indices and tables