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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.
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...
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