Search Results for "simulated annealing"

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.

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.

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.

...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.

Search Agents - a framework for collaboration of meta-heuristic agents

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

The OptControlCentre (OCC) is an user-friendly software package for the optimization of dynamic systems in energy and chemical engineering. Optimization methods include SQP methods as well as a stochastic approach using Simulated Annealing.

This projects implements various optimization heuristics and meta-heuristics (such as local search, VND, GRASP, Simulated Annealing, and more still to come) finding solutions on the post enrolment course timetabling problem.

PyOptFrame-LEGACY is a Python version of OptFrame v2, proposed in 2011, now superseeded in 2021 by v5 on GitHub and PIP. The main objective is to provide the same interface to OptFrame C++ optimization framework, including classic metaheuristics such as genetic algorithms, simulated annealing, variable neighborhood search, first/best/multi-improvement, hill climbing, and multi-objective methods such as nsga-ii. See NEWEST version v5 on GitHub and PIP. Please try Official pyoptframe on https://pypi.org/project/optframe/ for OptFrame v5 (last updated 2022).