Multiparametric Multiextremal Optimization Algorithm Software Simplex Evolution for MATLAB and Simintech Libraries

This article is devoted to some results of research on improving the multiparameter multiextremal optimization algorithm in proposed author”s evolutionary algorithm version Simplex Evolution program. The research was conducted in the Visual Studio software environment using the C# programming language and its multithreaded method. For this solution, the “main” thread is the user interface for setting and processing results, and all other threads are parallel interacting elements groups that move in the search space to the best multi-extreme function solution. At each evolutionary algorithm step, elimination is performed for each stream. The essence of which is to remove the worst solutions results, which are replaced by new, randomly selected starting points. The target function acts as a measure of fitness for the environment. The probability of finding a global extreme depends on both the number of search points in the groups (population size) and the number of streams. The results obtained show a significant increase in the probability of finding a global extremum and an increase in the algorithm's performance in parallel multithreaded calculations compared to single-threaded ones. The Simplex Evolution algorithm shell is intended for implementation in MATLAB and SimInTech libraries as a built-in function.