A framework for single/multi-objective optimization with metaheuristics

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

Evolutionary & genetic algorithms for Julia

[ICML 2020] Efficient Continuous Pareto Exploration in Multi-Task Learning

A very fast, 90% vectorized, NSGA-II algorithm in matlab.

Spatial Containers, Pareto Fronts, and Pareto Archives

OptFrame - C++17/C++20/C++23 Optimization Framework in Single or Multi-Objective. Supports classic metaheuristics and hyperheuristics: Genetic Algorithm, Simulated Annealing, Tabu Search, Iterated Local Search, Variable Neighborhood Search, NSGA-II, Genetic Programming etc. Examples for Traveling Salesman, Vehicle Routing, Knapsack Problem, etMu

🤹 MultiTRON: Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems, accepted at ACM RecSys 2024.

Multi-Objective PSO (MOPSO) in MATLAB

pfevaluator: A library for evaluating performance metrics of Pareto fronts in multiple/many objective optimization problems

NSGA-III: Non-dominated Sorting Genetic Algorithm, the Third Version — MATLAB Implementation

Genetic Algorithm (GA) for a Multi-objective Optimization Problem (MOP)

An R package for multi/many-objective optimization with non-dominated genetic algorithms' family

Non-dominated Sorting Genetic Algorithm II (NSGA-II) in MATLAB

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

[AAAI-26] MPaGE: Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization

Minimal Policy Search Toolbox

Official repository of "Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models" [ICML 2023]

(Code) Multi-objective Sparrow Search Optimization for Task Scheduling in Fog-Cloud-Blockchain Systems

Implementation of NSGA-II in Python

A tutorial for the famous non dominated sorting genetic algorithm II, multiobjective evolutionary algorithm.

A Multi-objective community detection library written in Rust exposed to python through PyO3

🌳MultiLGBM🌳: A simple multi-objective regression example to show how to trade-off objectives on the Pareto front with a single LGBM model.

A set of ant colony system and max-min ant system based algorithms for the single-objective MinMax Multiple Traveling Salesman Problem (mTSP) and for the bi-objective mTSP

Python bindings for OptFrame C++ Functional Core

Advanced choice modeling with multidimensional utility representations.

This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.

This repository contains source code for the four investigated ACO algoritms for the bi-objective Multiple Traveling Salesman Problem. For more details, see this paper "Necula, R., Breaban, M., Raschip, M.: Tackling the Bi-criteria Facet of Multiple Traveling Salesman Problem with Ant Colony Systems. ICTAI, (2015)" (https://ieeexplore.ieee.org/document/7372224).

A Memetic Procedure for Global Multi-Objective Optimization

A collection of handy functions for multi-objective optimization written in C with a python wrapper