Search Results for "simulated annealing"
Sort By:
Showing 35 open source projects for "simulated annealing"
View related business solutions-
Gen AI apps are built with MongoDB AtlasMongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
-
Network Discovery Software | JDisc DiscoveryJDisc Discovery is a comprehensive network inventory and IT asset management solution designed to help organizations gain clear, up-to-date visibility into their IT environment. It automatically scans and maps devices across the network, including servers, workstations, virtual machines, and network hardware, to create a detailed inventory of all connected assets. This includes critical information such as hardware configurations, software installations, patch levels, and relationshipots between devices.
-
1
Adaptive Simulated Annealing (ASA)
simulated annealing optimization and importance-sampling
Adaptive Simulated Annealing (ASA) is a C-language code that finds the best global fit of a nonlinear cost-function over a D-dimensional space. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems. -
2
Superstaq
Quantum software platform that is optimized across the quantum stack
This repository is the home of the Superstaq development team's open-source work. Our quantum software platform is optimized across the quantum stack and enables users to write quantum programs in Cirq or Qiskit and target a variety of quantum computers and simulators. -
3
ntuacarlo
A open-source implementation library of optimization algorithms
ntuacarlo: library of open-source implementation of optimization algorithms for Matlab/GNU Octave The library implements matlab functions for the following optimization algorithms: Simulated Annealing Particle Swarm Optimization Monte Carlo Exhaustive search The signature of the functions follow the same as the ga() function of Matlab. All the functions support lower and upper bounds, linear and non-linear constraints, and integer variables.Octave -
4
Hypercube
Graph visualizing tool
Hypercube is a tool for visualizing DOT (graphviz), GML, GraphML, GXL and simple text-based graph representations as SVG and EPS images. Hypercube comes with a Qt based GUI application and a Qt-independent command-line tool. It uses a simulated annealing algorithm to lay out the graph, that can be easily parameterized to achieve the desired look. The main development goals are portability and easy usage rather than high performance and complexity. -
Curtain LogTrace File Activity MonitoringCurtain LogTrace File Activity Monitoring is an enterprise file activity monitoring solution. It tracks user actions: create, copy, move, delete, rename, print, open, close, save. Includes source/destination paths and disk type. Perfect for monitoring user file activities.
-
5
Algorithms Math Models
MATLAB implementations of algorithms
...The repository gathers implementations and case studies across many topics commonly used in contest solutions: optimization (linear, integer, goal and nonlinear programming), heuristic and metaheuristic methods (simulated annealing, genetic algorithms, immune algorithms), neural networks and time-series methods, interpolation and regression, graph theory, cellular automata, grey systems, fuzzy models, partial/ordinary differential equations, and multivariate analysis, among others. The codebase is organized into topic folders (e.g., HeuristicAlgorithm, IntegerProgramming, NeuralNetwork, TimeSeries) and includes dozens of worked examples and links to textbook/source materials that the author used to assemble the collection. -
6
ePnR
ePnR is an IC block standard cell placement & routing tool
...Placement follows initially the cell call order in the SPICE like circuit input netlist. However, a placement optimization, aiming at minimum weighted accumulated wire length, by simulated annealing is available. Routing consists of channel routing as first step. If un-routed connections are left, Maze routing can (optionally) be applied. ePnR does not guarantee completely finished routing. However, un-routed connections will be left with rubberband connections and marked start and end points for subsequent manual routing using a third party layout editor. ePnR outputs in CIF 2.0 and GDS stream format readable by e.g. the free KLayout editor. -
7
Cuda Simulated Annealing GPU Route Plan
An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using
An Optimized GPU-Accelerated Route Planning of Multi-UAV Systems Using Simulated Annealing Article CUDA CODE Usage of multiple unmanned aerial vehicles (UAV) in a certain mission makes flight route planning more complicated and slower. In order to obtain better performance, in the literature, most of the researchers propose using evolutionary algorithms and artificial intelligence approaches based on heuristics as optimization techniques. -
8
popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known network flow and other graph algorithms. A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. ... -
9
Solid Python
A comprehensive gradient-free optimization framework written in Python
Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them. -
The only CRM built for B2CKlaviyo unifies your customer profiles by capturing every event, and then lets you orchestrate your email marketing, SMS marketing, push notifications, WhatsApp, and RCS campaigns in one place. Klaviyo AI helps you build audiences, write copy, and optimize — so you can always send the right message at the right time, automatically. With real-time attribution and insights, you'll be able to make smarter, faster decisions that drive ROI.
-
10
MUSE
A Multi-algorithm Collaborative Structure-prediction Environment
MUSE is short for Multi-algorithm-collaborative Universal Structure-prediction Environment, which was developed for easy use in structure prediction of materials under ambient or extreme conditions, such as high pressure. It was written in Python and organically combined the multi algorithms including the evolutionary algorithm, the simulated annealing algorithm and the basin hopping algorithm to collaboratively search the global energy minimum of materials with the fixed stoichiometry. After introduced the competition in all the evolutionary and variation operators, the evolution of the crystal population and the choice of the operators are self-adaptive automatically, i.e. the crystal population undergoes the self-adaptive evolution process. ... -
11
Baseball Stat Optimization
Predict baseball team wins from individual stats
What contributes to a winning team? How much is a hit worth, versus a walk? To address these questions, we consider the team stats and optimize the weights of each statistic to wins using the simulated annealing method of parameter optimization. Individual stats (such as hits, strikeouts, stolen bases) and not collective stats (runs, runs allowed, ERA) are considered, although the code can be trivially modified to consider any stats. From these stats, we predict a number of wins, and optimize parameters until the differences are minimized according to the popular "chi-squared" measure. ... -
12
program.MODULAR
MODULAR: Autonomous Computation of Modularity
...The software can identify and define modules by two different modularity metrics widely used in studies with different types of networks such as social, ecological and biochemical networks. In order to find the network partition that maximizes modularity, the software offers five optimization methods to the user: simulated annealing, fast greedy, spectral partitioning, a hybrid of fast greedy and simulated annealing, and a hybrid of spectral partitioning and simulated annealing. The software is implemented in C language and it uses the igraph library for complex network research. -
13
Opt4J
Modular Java framework for meta-heuristic optimization
...It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary meta-heuristic optimization algorithms. For this purpose, Opt4J relies on a module-based implementation and offers a graphical user interface for the configuration as well as a visualization of the optimization process. -
14
This software searches the potential energy surface of small to medium size atomic systems for global minima using quantum ab initio techniques. It performs bond rotations and molecule translations and rotations on a Linux cluster with MPI.
-
15
The School Therapist Scheduling Program
To enable school therapists to handle their entire schedule.
...Meeting the required criteria and conditions of the student as well as the therapist makes the conditions for scheduling within the educational environment all the more difficult. To solve this problem, this program uses the simulated annealing algorithm and a group based GUI to ensure a user friendly, yet functional interface. -
16
EmulMultiFit
Simultaneously fit SAS data with polydisperse core-shell-shell spheres
Keywords: -simultaneously fit several SAXS and SANS data sets with polydisperse (Schultz-Zimm or Gaussian distribution f(R)) spherical core-shell-shell nanoparticles -analytical expressions are used for from factor F(Q) and its integral over f(R), no numerical integration required -absolute units -Mathematica is required via console (MathKernel) -Mathematica's local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters are possible -Monodisperse(!) hard sphere structure factor can be used, too -long computation times (depending on problem size and amount of constraints) from hours to a few days are possible -non-parallelized code -
17
XNDiff
X-ray and Neutron powder pattern simulation analysis
Keywords (XNDiff): -SAXS -SANS -absolute units -core (double)shell crystalline nanoparticles -with a parallelepidal shape -particle assemblies -powder and ensemble average -C/C++ -Unix -OpenMP -HPC Cluster Keywords (BatchMultiFit): -simultaneous fits for several SAXS and SANS curves with simulation data from XNDiff -SANS data can be smeared with dq values from experimental data sets or analytical functions -Mathematica console -local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters -parallelized (typ. 4-8 threads) TODO (BatchMultiFit): -read and use errorbars from experimental data sets -allow different q-ranges for different data sets in the fits -rewrite and test in Python using e.g. the lmfit module: https://pypi.python.org/pypi/lmfit/ to get rid of Mathematica and to run it on HPC clusters -
18
ABM-Calibration-SensitivityAnalysis
Codes and Data for Calibration and Sensitivity Analysis of ABM
...<http://jasss.soc.surrey.ac.uk/xx/x/x.html> Methods/Techniques used are: a. Parameter fitting: 1. Full Factorial Design 2. Simple Random Sampling 3. Latin Hypercube Sampling 4. Quasi-Newton Method 5. Simulated Annealing 6. Genetic Algorithm 7. Approximate Bayesian Computation b. Sensitivity Analysis: 1. Local SA 2. Morris Screening 3. DoE 4. Partial (Rank) Correlation Coefficient 5. Standardised (Rank) Regression Coefficient 6. Sobol' 7. eFAST 8. FANOVA Decomposition Have also a look on our other projects: http://www.uni-goettingen.de/de/315075.html -
19
AMBIENT
Find active modules in metabolic networks using high-throughput data
...If you wish to use the version used in the paper it is v0.6.3, however I recommend using the latest version which works in the same way but with additional options and has stability and performance improvements. Thanks for your interest! AMBIENT (Active Modules for Bipartite Networks) is a Python module that uses simulated annealing to find areas of a metabolic network (modules) that have some consistent characteristic. AMBIENT does not require predefined pathways and gives highly specific predictions of affected areas of metabolism. For example, scores for reactions based on transcriptional data of their annotated encoding genes can be used in the network and modules of coordinated expression changes can be found. ... -
20
Search Agents - a framework for collaboration of meta-heuristic agents
-
21
Clever Algorithms
Clever Algorithms: Nature-Inspired Programming Recipes
...Each entry follows a consistent template: motivation, strategy, pseudocode, parameter choices, variations, and references, making it easy to compare approaches. The catalog spans evolutionary algorithms, swarm intelligence, immune systems, simulated annealing, tabu search, and other metaheuristics, plus guidance on when and how to tune them. Example implementations and worked problems show how to encode solutions, define fitness, and balance exploration with exploitation. The emphasis is on pragmatism—enough theory to understand why an algorithm works, and enough detail to get it running in your environment. ... -
22
Drawnneal helps you create draws for your tournament / league or schedules for appointments. It optimizes for all sorts of things using simulated annealing.
-
23
OptLib
C nonlinear optimization library
[PROJECT MIGRATED TO GIT-HUB] OptLib is a library of nonlinear optimization routines focused on the use of stochastic methods, including Simulated Annealing, Genetic Algorithms, and Monte Carlo. Routines are parallelized using MPI. -
24
Open Metaheuristic (oMetah) is a library aimed at the conception and the rigourous testing of metaheuristics (i.e. genetic algorithms, simulated annealing, ...). The code design is separated in components : algorithms, problems and a test report generator
-
25
Simulated annealing for cell tracking (assignment problem in 4D) with scheme for removing low energy nodes from consideration ("quenching").