aeroopt 0.1.1

A multi-objective optimization tool for engineering applications.

AeroOpt (aeroopt)

A multi-objective (and single-objective) optimization framework for engineering workflows.

This framework is developed to handle the complex needs of:

• calling external tools for evaluation
• parallel evaluation
• pre- and post-processing of the population
• user manipulation of the population

This framework is also developed for other purposes, such as:

• hybridization of different optimization algorithms, machine learning tools, etc.
• adaptive sampling

Features

• Problems and data: Problem, Individual, Database; constraint strings and custom constraint callables; databases can be serialized to JSON / Excel.
• Evaluation: built-in Python objectives or external executables; MultiProcessEvaluation for parallel runs (examples cover Linux and Windows).
• Optimization loop: OptBaseFramework with pluggable PreProcess / PostProcess; examples show population pre/post-processing and user hooks.
• Evolutionary algorithms: NSGA-II, NSGA-III, RVEA, MOEA/D, differential evolution (MODE-style), NRBO, and more; DominanceBasedAlgorithm supplies non-dominated sorting, crowding, parent selection, and related helpers.
• Surrogates and hybrids: SAO and SBO in aeroopt.optimization.hybrid; aeroopt.utils.surrogate defines surrogate interfaces (e.g. Kriging with SMT or similar backends).
• Analysis and utilities: AnalyzeDatabase in aeroopt.analysis.analyze_database; standard test functions in aeroopt.utils.benchmark (e.g. Rastrigin, ZDT suites).

For richer visualization and decision-making around multi-objective optimization, see pymoo.

Requirements

• Python ≥ 3.9
• Core dependencies: numpy, scipy, scikit-learn (see pyproject.toml)

Installation

Repository: https://github.com/swayli94/AeroOpt

From PyPI:

pip install aeroopt

Editable install from a clone of this repository:

pip install -e .

Package layout

Package	Role
aeroopt.core	Problem, Individual, Database, settings types, MultiProcessEvaluation, logging and path helpers
aeroopt.optimization	OptBaseFramework, PreProcess / PostProcess, algorithm settings, Opt* drivers, MOEA utilities
aeroopt.optimization.stochastic	Implementations of NSGA-II/III, RVEA, MOEA/D, DE, NRBO, etc.
aeroopt.optimization.hybrid	SAO, SBO, and related post-processing
aeroopt.analysis	Database analysis such as AnalyzeDatabase (aeroopt.analysis.analyze_database)
aeroopt.utils	benchmark, surrogate

Quick import:

import aeroopt
from aeroopt.core import Problem, Database, MultiProcessEvaluation
from aeroopt.optimization import OptNSGAII, SettingsNSGAII

Examples (example/)

Many scripts prepend the repository root to sys.path so they run without installing the package; remove that block if you already use pip install aeroopt.

Folder	Script	Summary
1-database-io	example_core_functions.py	Problem and database setup; JSON / Excel I/O
2-mp-evaluation	example_mpEvaluation.py	Parallel evaluation with built-in and external executables
3-database-evaluation	example_database_evaluation.py	Database.evaluate_individuals: serial vs. multiprocessing, external scripts
4-pre-process	example_pre_process.py	Custom PreProcess and candidate-database repair
5-evolutionary-algorithm	example_dominance_based_algorithm.py	DominanceBasedAlgorithm dominance and selection tools
5-evolutionary-algorithm	example_pareto_analysis.py	Pareto-focused analysis (pairs with multi-objective examples)
6-single-objective-optimization	example_soo.py	Single-objective comparison: NSGA-II, DE, NRBO, etc.
7-multi-objective-optimization	example_nsgaii.py and others	ZDT benchmarks with NSGA-II, DE, NSGA-III, RVEA, MOEA/D; see README.md in that folder
8-surrogate-hybrid-optimization	example_sao.py, example_sbo.py	Surrogate-assisted and surrogate-based hybrid optimization
8-surrogate-hybrid-optimization	example_kriging.py	Kriging / surrogate usage