Discussion of article "Population optimization algorithms"
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New article Population optimization algorithms has been published:
This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.
When optimizing trading systems, the most exciting things are metaheuristic optimization algorithms. They do not require knowledge of the formula of the function being optimized. Their convergence to the global optimum has not been proven, but it has been experimentally established that in most cases they give a fairly good solution and this is sufficient for a number of problems.
A lot of OAs appeared as models borrowed from nature. Such models are also called behavioral, swarming or population, such as the behavior of birds in a flock (the particle swarm algorithm) or the principles of the ant colony behavior (ant algorithm).
Population algorithms involve the simultaneous handling of several options for solving the optimization problem and represent an alternative to classical algorithms based on motion trajectories whose search area has only one candidate evolving when solving the problem.
Author: Andrey Dik