提出一种带有随机变异的动态差分进化算法。
The dynamic Differential Evolution (DE) algorithm with random mutation was proposed.
提出一种新的动态调整子种群个体数目的并行差分进化算法。
A novel parallel differential evolution (NPDE) algorithm with dynamically adjusting the number of subpopulation individuals was proposed for solving complex optimization problems.
提出一种基于群体适应度方差自适应二次变异的差分进化算法。
A new adaptive second mutation differential evolution algorithm (ASMDE) based on the variance of the population's fitness is presented.
利用差分进化算法求解该优化问题，并利用可行性规则处理约束。
The optimization problem was solved by differential evolution and the constraints were handled by feasibility-based rule.
在简要介绍基本差分进化算法的基础上，可总结出差分算法家族系列。
Based on the introduction of the basic Differential Evolution Algorithm, this paper sums up the series of the Differential Evolution Algorithms.
利用粒子群算法和差分进化算法的优点，可以获得测向问题的全局最优解。
The ADPSO is a global optimization algorithm for direction finding, which takes advantage of the merits of differential evolutionary algorithm and particle swarm algorithm.
在基本差分进化算法中，融入递增二次函数交叉算子以增加算法的收敛速度。
For the basic differential evolution algorithm, the increasing quadratic function crossover operator was added to increase the convergence speed.
该算法采用差分进化算法对目标函数自动寻优，求得分离矩阵，从而分离出信号。
By using differential evolution, the proposed algorithm finds the global optimal solution of objective function and obtains the separating matrix so that the signals are separated successfully.
为了提高差分进化算法(DEA)的收敛速度和寻优精度，提出了一种改进的差分进化算法。
To improve the optimum speed and optimization accuracy of Differential Evolution Algorithm (DEA), an improved DEA was proposed.
针对差分进化(DE)算法收敛早熟与计算效率不理想的问题，提出一种改进的差分进化算法。
An improved Differential Evolution (DE) algorithm was proposed to solve the problem of premature convergence and improve the computational efficiency of DE.
提出了一种基于差分进化(DE)算法的核磁共振弛豫信号多指数反演新方法。
A new multi-exponential inversion method for NMR relaxation signals is presented and tested, which is based on differential evolution (DE) algorithm.
提出了一种基于差分进化(DE)算法的核磁共振弛豫信号多指数反演新方法。
A new multi-exponential inversion method for NMR relaxation signals is presented and tested, which is based on differential evolution (DE) algorithm.
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