Differential Evolution Algorithm In Models Of Technical Optimization

At present, evolutionary optimization algorithms are increasingly used in the development of new technological processes. Evolutionary algorithms often allow the optimization procedure to be performed even in cases where classical optimization algorithms fail (e.g. gradient methods) and where an acceptable solution is sufficient to solve the optimization task. The article focuses on possibilities of using a differential evolution algorithm in the optimization process. This algorithm is often referred to in the literature as a global optimization procedure. However, we show by means of a practical example that the convergence of the classic differential algorithm to the global extreme is not generally assured and is largely dependent on the specific cost function. To remove this weakness, we designed a modified version of the differential evolution algorithm. The improved version, named the modified differential evolution algorithm, is described in the article. It is possible to prove asymptotic convergence to the global minimum of the cost function for the modified version of the algorithm.