The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper we consider the Differential Evolution (DE) algorithm by using fuzzy logic to make dynamic changes in the mutation parameter (F), and this modification of the algorithm we call the Fuzzy Differential Evolution algorithm (FDE). A comparison of the FDE algorithm using type 1 fuzzy logic and interval type-2 fuzzy logic is performed for a set of Benchmark functions.
In this article we use the Differential Evolution (DE) algorithm which using fuzzy logic has the mutation parameter (F) dynamic, this modification of the algorithm we call Fuzzy Differential Evolution algorithm (FDE), a comparison algorithm using type 1 fuzzy logic and interval type 2 fuzzy logic is performed by for a set of functions Benchmark.
In this paper a fuzzy system for dynamic adaptation of parameters is being developed for the optimization of the Differential Evolution (DE) algorithm. The main purpose of this work is to improve the Differential Evolution algorithm in which dynamic parameters of crossover and mutation will change in order to obtain better results. The Differential Evolution algorithm is an optimization method which...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.