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Differential evolution, termed DE, is a novel and rapidly developed evolution computation in recent year. There are some advantages of DE, including simple structure, easy use and rapid convergence speed. Besides, DE can be also applied on complex optimization problem. However, there are some problems, such as premature convergence and stagnation, remaining in DE algorithm. To overcome those disadvantages,...
The 0-1 knapsack problem (KP) is one of the classical NP-hard problems with binary decision variables. The traditional differential evolution (DE) is an effective stochastic parallel search evolutionary algorithm for global optimization based on real valued crossover and mutation operations in continuous space. To solve KPs, based on DE, a discrete binary version of differential evolution (DBDE) was...
A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the...
Differential evolution (DE) is well known as a simple and efficient algorithm for global optimization over continuous spaces. This article provides a simple mathematical model of the underlying evolutionary dynamics of a one-dimensional DE. The model relates the search process of DE with the classical gradient descent search and also analyzes the convergence behavior of a DE population, very near...
The Differential Evolution (DE) is a stochastic population-based search method for global optimization over continuous spaces. This paper presents an efficient strategy for self-adapting control parameters in Differential Evolution to solve real-parameter optimization problems. The proposed strategy introduces an adaptive mechanism at the individual level based on Cauchy distribution (CD) where the...
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