metaheuristics

There are 563 repositories under metaheuristics topic.

  • incubator-kie-optaplanner

    apache/incubator-kie-optaplanner

    AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.

    Language:Java3.3k1690953

  • ljvmiranda921/pyswarms

    A research toolkit for particle swarm optimization in Python

    Language:Python1.3k40231333

  • thieu1995/mealpy

    A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)

    Language:Python92115145189

  • MaxHalford/eaopt

    :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)

    Language:Go890322296
  • jenetics

    jenetics/jenetics

    Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization

    Language:Java85541507154

  • esa/pagmo2

    A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.

    Language:C++84435238162

  • 100/Solid

    🎯 A comprehensive gradient-free optimization framework written in Python

    Language:Python57512761

  • jMetal/jMetalPy

    A framework for single/multi-objective optimization with metaheuristics

    Language:Python53322109151

  • jMetal/jMetal

    jMetal: a framework for multi-objective optimization with metaheuristics

    Language:Java52357260405

  • 7ossam81/EvoloPy

    EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.

    Language:Jupyter Notebook4532445227

  • optapy/optapy

    OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

    Language:Java278107321

  • EMI-Group/evox

    Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

    Language:Python22553037

  • JingweiToo/Wrapper-Feature-Selection-Toolbox

    This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.

    Language:MATLAB17231136
  • biteopt

    avaneev/biteopt

    Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP

    Language:C++1481459

  • dungtran209/Modelling-and-Analysis-of-a-Vehicle-Routing-Problem-with-Time-Windows-in-Freight-Delivery

    A MSc's Dissertation Project which focuses on Vehicle Routing Problem with Time Windows (VRPTW), using both exact method and heuristic approach (General Variable Neighbourhood Search)

    Language:Python1342137

  • JuliaNonconvex/Nonconvex.jl

    Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

    Language:Julia11458810

  • rwuilbercq/Hive

    Artificial Bee Colony Algorithm in Python.

    Language:Python1116252
  • paradiseo

    nojhan/paradiseo

    An evolutionary computation framework to (automatically) build fast parallel stochastic optimization solvers

    Language:C++99105434

  • donfaq/VRPTW

    Solving VRPTW with metaheuristics

    Language:Python793223

  • optframe/optframe

    OptFrame - C++17 (and C++20) 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, etc.

    Language:C++7471110

  • alberto-santini/adaptive-large-neighbourhood-search

    ALNS header-only library (loosely) based on the original implementation by Stefan Ropke.

    Language:C++645132
  • Chips-n-Salsa

    cicirello/Chips-n-Salsa

    A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms

    Language:Java60216537

  • amineremache/qbso-fs

    Python implementation of QBSO-FS : a Reinforcement Learning based Bee Swarm Optimization metaheuristic for Feature Selection problem.

    Language:Jupyter Notebook565317

  • ashishpatel26/Amazing-Collection-Vehicle-Routing-Problem

    Amazing Collection Vehicle Routing Problem

    482016

  • snowberryfield/printemps

    C++ metaheuristics modeler/solver for general integer optimization problems.

    Language:C++482104
  • ypea

    smkalami/ypea

    Yarpiz Evolutionary Algorithms Toolbox for MATLAB

    Language:HTML475328

  • acco93/filo

    A Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP.

    Language:C++455411

  • jakobbossek/ecr2

    ecr: Evolutionary Computation in R (version 2)

    Language:R4381268

  • thomasWeise/jsspInstancesAndResults

    A repository with a data set including instances and results from literature for the Job Shop Scheduling Problem (JSSP). While the raw data is provided as text files, it is also compiled in an R package with an API around it.

    Language:R40607

  • kkg1999/MetaheuristicOptimization

    Different meta-heuristic optimization techniques for feature selection

    Language:Python390124
  • Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-

    aliasgharheidaricom/Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-

    In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html

    Language:MATLAB382011

  • JingweiToo/Advanced-Feature-Selection-Toolbox

    This toolbox offers advanced feature selection tools. Several modifications, variants, enhancements, or improvements of algorithms such as GWO, FPA, SCA, PSO and SSA are provided.

    Language:Python321213

  • afourmy/swap

    A Solver for the Wavelength Assignment Problem (RWA) in WDM networks

    Language:CSS31515

  • SeyedMuhammadHosseinMousavi/ABC-PSO-Path-Planning

    ABC+PSO Path Planning

    Language:MATLAB31105

  • mykeels/MSearch

    A C# Library to aid programming for meta-heuristics

    Language:C#30416

  • Evolutionary-Optimization-Laboratory/rmoo

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

    Language:R29377