Freeware Listing: Annealing

Two programs are attached: sa_demo demonstrates how the simualted annealing works for simple functions, while sa_mincon solves a welded beam design problem using simulated annealing, which can easily be used to solve other constrained optimization problems in engineering design..

  • Platform: Matlab, Scripts

This is a Java application that demonstrates the Simulated Annealing algorithm with an attack on the "traveling salesman" problem. What is simulated annealing? Simulated annealing is a technique, which was developed to help solve large combinatorial optimization problems. It is based on probabilistic methods that avoid being stuck at local (non-global) minima. It has proven to be a simple but powerful method for large-scale combinatorial optimization. For practical purposes, simulated annealing has solved the famous traveling salesman problem: find the shortest of N! paths connecting N cities.

  • Platform: Not Applicable

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..

  • Platform: Linux, Mac OS X, Unix, WinOther

simulatedannealing() is an optimization routine for traveling salesman problem. Any dataset from the TSPLIB can be suitably modified and can be used with this routine. A detailed description about the function is included in "Simulated_Annealing_Support_Document.pdf." A GUI is used with the core function to visualize and to vary annealing parameters. Four sample data set from TSPLIB is provided. You can create your own data set by following a simple procedure given in the supporting document. You can access the TSPLIB by following this link http://www.iwr.uni-heidelberg.de/groups/co...tware/TSPLIB95/ Feel free to drop in your comments, suggestions, bugs and mistakes in the algorithm.

  • Platform: Matlab, Scripts
  • tspsa
  • License: Freeware
  • Rating

Simulated Annealing Optimization m-file The program set can be used to solve TRAVELING SALESMAN PROBLEMS from the TSPLIB..

  • Platform: Matlab, Scripts

anneal Minimizes a function with the method of simulated annealing (Kirkpatrick et al., 1983) ANNEAL takes three input parameters, in this order: LOSS is a function handle (anonymous function or inline) with a loss function, which may be of any type, and needn't be continuous. It does, however, need to return a single value. PARENT is a vector with initial guess parameters. You must input an initial guess. OPTIONS is a structure with settings for the simulated annealing. If no OPTIONS structure is provided, anneal uses a default structure. OPTIONS can contain any or all of the following fields (missing fields are filled with default values): Verbosity: Controls output to the screen.

  • Platform: Matlab, Scripts

A collection of different methods for implementing in C++ polycrystalline growth for metals during annealing or recrystallization..

  • Platform: WinOther

Hotstop is scientific software that allows to extract experimental ion stopping force curves from RBS spectra (as well as to stimate uncertainities) It uses Simulated Annealing and Bayesian Inference algorithms..

  • Platform: Linux, Unix, WinOther

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..

  • Platform: Linux, Mac OS X, WinOther

This submission contains the demo files used in the Global Optimization with MATLAB webinar: http://www.mathworks.com/wbnr43346 MultStart Demos * Peaks Minimization * Nonlinear Curve Fitting GlobalSearch Demos * Peaks Minimization * Volumentric Efficiency Maximization Simulated Annealing Demos * Peaks Minimization * Eight Queens Problem * Galactic Traveling Salesman Pattern Search Demos * Peaks Minimization * Mount Washington Demo Genetic Algorithm Demos * Peaks Minmiziation * Rastrigins Function Minimization * Partical Swarm Example * Multiobjective Genetic Algorithm Comparison of Solvers on Rastrigins Function * Comparison of Multiple Solvers on Rastrings.

  • Platform: Matlab, Scripts

Introduction to Stochastic Search and Optimization is an overview of the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The book may serve as either a reference book for researchers and practitioners or as a textbook, the latter use being supported by exercises at the end of every chapter and appendix. The text covers a broad range of the most widely used stochastic methods, including: Random search* Recursive linear estimation* Stochastic approximation* Simulated annealing* Genetic and evolutionary algorithms* Machine (reinforcement) learning* Model selection* Simulation-based optimization* Markov chain Monte Carlo* Optimal experimental design The MATLAB code here is in support of the book.

  • Platform: Matlab, Scripts

GODLIKE (Global Optimum Determination by Linking and Interchanging Kindred Evaluators) compiles various population-based global optimization schemes. It handles both single- and multi-objective optimization, simply by adding additional objective functions. It solves optimization problems using relatively basic implementations of a genetic algorithm, differential evolution, particle swarm optimization and adaptive simulated annealing algorithms. The script is primarily intended to increase robustness, not efficiency as it usually requires more function evaluations than any of the algorithms separately.

  • Platform: Matlab, Scripts

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. This provides an alternative to pathway/gene set enrichment analyses which is simultaneously more flexible and more specific.

  • Platform: WinOther

Gabedit is a graphical user interface to computational chemistry packages like Gamess-US, Gaussian, Molcas, Molpro, MPQC, OpenMopac, Orca, PCGamess and Q-Chem

It can display a variety of calculation results including support for most major molecular file formats. The advanced "Molecule Builder" allows to rapidly sketch in molecules and examine them in 3D. Graphics can be exported to various formats, including animations.



features

* Gabedit can Creates input file for GAMESS(US), GAUSSIAN, MOLCAS, MOLPRO , MPQC, OpenMopac, Orca, PCGamess and Q-Chem.
* Gabedit can graphically display a variety of Gamess-US, Gaussian, Molcas, Molpro, MPQC, OpenMopac, Orca, PCGamess, Q-Chem and (partially) ADF calculation results, including the following
o Molecular orbitals.

  • Platform: WinOther

Evolutionary optimization methods, inspired from natural processes, have shown good performance in solving complex optimization problems. For example, genetic algorithms (inspired from biological evolution of human and other species), ant colony optimization (based on ants effort to find optimal path to the food source) and simulated annealing (based on real annealing process in which a substance is heated over its melting point and then cooled to reach to a crystalline lattice) are widely used to solve engineering optimization problems. The proposed evolutionary optimization algorithms are generally inspired by modeling the natural processes and other aspects of species evolution, especially human evolution, are not considered.

  • Platform: Matlab, Scripts

These files are the MATLAB code for "Imperialist Competitive Algorithm (ICA)" which in some papers is referred by "Colonial Competitive Algorithm CCA". Evolutionary optimization methods, inspired from natural processes, have shown good performance in solving complex optimization problems. For example, genetic algorithms (inspired from biological evolution of human and other species), ant colony optimization (based on ants effort to find optimal path to the food source) and simulated annealing (based on real annealing process in which a substance is heated over its melting point and then cooled to reach to a crystalline lattice) are widely used to solve engineering optimization problems.

  • Platform: Matlab, Scripts

This software contain one example By running the program test1.m as it is in the default folder the economic dispatch problem is solved. The allocation minimum fuel cost and transmission losses can be determined. The Example system is taken from the book Power System Analysis by Prof Haadi Sadaat Example 7.8 The results Initial temperature: 100 Final temperature: 9.04626e-007 Consecutive rejections: 12248 Number of function calls: 248012 Total final loss: 36912.2 F =3.6912e+004 P1 = 28.2912 10.0000 118.9586 118.6796 230.7620 212.7404 Pl = 19.4318 Elapsed time is 40.000000 seconds. ALL THE BEST.

  • Platform: Matlab, Scripts

GODLIKE (Global Optimum Determination by Linking and Interchanging Kindred Evaluators) is a generization of various population-based global optimization schemes. Also, it handles both single- and multi-objective optimization, simply by adding additional objective functions. GODLIKE solves optimization problems using relatively basic implementations of a genetic algorithm, differential evolution, particle swarm optimization and adaptive simulated annealing algorithms. Its power comes from the fact that these different algorithms run simultaneously (linked), and members from each population are occasionally swapped (interchanged) to decrease the chances of convergence to a local minimizer.

  • Platform: Matlab, Scripts

Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks, including the gene regulatory network. Due to several NP-hardness results on learning static Bayesian network, most methods for learning DBN are heuristic, that employ either local search such as greedy hill-climbing, or a meta optimization framework such as genetic algorithm or simulated annealing. We present GlobalMIT, a toolbox for learning the globally optimal DBN structure using a recently introduced information theoretic based scoring metric named mutual information test (MIT). Under MIT, learning the globally optimal DBN can be efficiently achieved in polynomial time.

  • Platform: Matlab, Scripts

Case of study: "Capacity Energy Storage Solution". Dual problem of SMES, replacing inductors with capacities..

  • Platform: Matlab, Scripts

Annealing: Freeware | All

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