Simulated annealing is a computational algorithm for optimization. It mimics the physical process of thermal annealing in which a metal is heated and then slowly cooled to settle into a highly ordered crystal structure. For common metals, the lowest energy state is already known. But the method is useful for other problems where the best state is not known and exhaustively searching all possible states is impractical. The method is applied by modeling the problem as a physical system with structure, energy, and temperature. This Python module implements simulated annealing so that it can be easily applied to a variety of problems. An example program is include to perform simulated annealing of the traveling salesman problem.
DAE Tools is cross-platform equation-oriented process modelling, simulation, and optimization software. Various types of processes (lumped or distributed, steady-state or dynamic) can be modelled and optimized. They may range from very simple to those which require complex operating procedures. Equations can be ordinary or discontinuous, where discontinuities are automatically handled by the framework. Model reports containing all information about a model can be exported in XML MathML format, automatically creating high-quality documentation. The simulation results can be visualized, plotted, and/or exported into various formats.
EO is a template-based, ANSI-C++ evolutionary computation library that helps you to write your own stochastic optimization algorithms quickly. Evolutionary algorithms form a family of algorithms inspired by the theory of evolution, and solve various problems. They evolve a set of solutions to a given problem in order to produce the best results. These are stochastic algorithms because they iteratively use random processes. The vast majority of these methods are used to solve optimization problems, and may be also called "metaheuristics". They are also ranked among computational intelligence methods, a domain close to artificial intelligence. With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems, from continuous to combinatorial ones.