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.
Traditional differential evolution (DE) algorithm has a tendency to suffer from premature convergence. In this paper, we proposed an improved DE based on dynamic mutation operator and opposition learning strategy. These mechanisms can expand the search area and is helpful to balance exploration and exploitation of DE. Numerical experiments demonstrate that our algorithm is effective.
In view of the Cuckoo Search algorithm easily fall in local optima, sometimes affect the defects of global search results. In this paper, by introducing a Hybrid-mutation operator to improve the performance of CS. The improved algorithm introduces Gauss-mutation and Differential Evolution into the cuckoos search their nests' progress. Which makes the algorithm easy to jump out of local optima. Through...
Inspired by Gaussian barebones differential evolution (GBDE), this study attempts to propose a new Gaussian mutation strategy, termed by GBDE/best-rand, to improve the solution accuracy. This study also proposes a hybrid crossover strategy, the hybridization of the binomial and arithmetic crossover strategies, for differential evolution (DE) to further balance the global search ability and convergence...
In differential evolution (DE) studies, there are many parameter adaptation methods, aiming at tuning the mutation factor $F$ and the crossover probability $\mathit {CR}$ . However, these methods still cannot resolve the issues of population premature convergence and population stagnation. To address these issues, in this paper, we investigate the population adaptation regarding population diversity...
Differential evolution (DE) is one of the evolutionally algorithms for solving optimization problems in a continuous space. DE has been widely applied to solve various optimization problems. Additionally, many modified DE algorithms have been developed in an attempt to improve search performance. In this paper, we propose island-based DE with varying subpopulation size. Island model is one of the...
During the search process of differential evolution (DE), each new solution may represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). This concurrent exploitation can interfere with exploration since the identification of a new more promising region depends on finding a (random) solution in that region which is better...
It is well known that mutation plays a very important role in the successful performance of Differential Evolution (DE) algorithm. The proposed scheme named Modified Random Localization (MRL) is based on strategically selecting the individuals from the entire search space rather than choosing them randomly as in basic DE. The corresponding DE variant named MRL-DE is analyzed on a set of 8 traditional...
Multi-swarm systems base their search on multiple sub-swarms instead of one standard swarm. The use of diverse sub-swarms increases performance when optimizing multi-modal functions. However, new design decisions arise when implementing multi-swarm systems such as how to select the initial positions and initial velocities, and how to coordinate the different sub-swarms. Starting from the relatively...
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.