Description: 采用差分进化算法解决一个有理数方程在给定范围内的求解问题。-using differential evolution algorithm to solve an equation with a given range of x Platform: |
Size: 5120 |
Author:林曦 |
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Description: 差分进化算法的一部分，希望对人有用，这个算法在处理数值反方面比较实用-Differential evolution part of the algorithm, and I hope to people useful, this algorithm in dealing with values of the more practical
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Size: 78848 |
Author:tang |
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Description: 基于差分进化和粒子群优化算法的混合优化算法，并且有查分算法的原理介绍。-Based on differential evolution and the particle swarm optimization algorithm hybrid optimization algorithm, and the principle of grey or algorithm is introduced. Platform: |
Size: 505856 |
Author:张大热 |
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Description: 基于差分进化和粒子群优化算法的混合优化算法，基于差分进化算法的定位算法，及其仿真。-Based on differential evolution and the particle swarm optimization algorithm hybrid optimization algorithm, evolutionary algorithm based on difference of localization algorithm, and its simulation. Platform: |
Size: 1310720 |
Author:张大热 |
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Description: 经典差分进化算法的MATLAB代码 毕业设计专用 每代的收缩代码 又要的请下载 求过-Classical differential evolution algorithm graduated from the MATLAB code design dedicated each generation of contraction of code have to download the begged Platform: |
Size: 2048 |
Author:小风 |
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Description: JADE差分进化的变异算法，针对不同的规模，差分进化算法的性能是不同的，这是另一个变异，有需要的请下载-Variation of differential evolution algorithm, for different scale, the performance of the differential evolution algorithm is different, this is another variation, the need to download the Platform: |
Size: 3351552 |
Author:小风 |
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Description: 该算法时LDPC码优化的好帮手。通过差分进化（DE）实现LDPC码度分布优化。给定码率，自动搜索最佳度分布。算法运算量较小，可以得到次优解。-The algorithm of LDPC code optimization is a good helper. Through differential evolution ( DE ) to realize LDPC code degree distribution optimization. Given rate, automatic search of optimal degree distribution. Algorithm can get smaller, suboptimal solution.
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Size: 8192 |
Author:chenziqiang |
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Description: 将离散约束优化问题转化为非负整数约束规划问题，开发求解该问题的离散差分进化算法。该算法采用基于混沌映射
的种群初始化、双版本变异和带随机扰动项的取整运算等新策略。针对非线性约束条件，给出惩罚基数的计算方法和连续映
射基函数的表达式，在此基础上设计处理非线性约束的自适应惩罚因子。提出一种刻画种群多样性的新测度——种群二次平
均基因距离及基于新测度的依概率混沌移民算子。将自适应罚函数法、依概率混沌移民操作与离散差分进化算法有机融合，
构造面向工程约束优化的混合离散差分进化算法。对 3 个离散约束优化实例进行验证，结果表明，混合算法具有良好的鲁棒
性且优于离散粒子群算法。应用混合算法求解斜齿圆柱齿轮传动优化设计问题，结果优于遗传算法及其改进算法、离散粒子
群算法，目标函数值较遗传算法及其改进算法分别下降41 和10-The constrained discrete optimization (CDO) is transfor med into a nonlinear constrained non-negative integer
programming (CNIP) which can be solved by the proposed discrete differential evolution (DDE) algorithm that adopts several
improvements such as the chaotic initialization of a population, the double-scheme mutation, and the integrating operator with
stochastic perturbation. Aiming at the nonlinear constraints, th e calculating approaches for the base penalty and the formula f or the
base function of continuous mapping are carried out, and self-adaptive penalty factors based on these notions for handling cons traints
are presented. It is studied that a novel measure, termed as a quasi re-averaging gene distance for a population, is employed t o depict
the diversity of the population and chaotic immigration operato rs depending on this measure a nd the probability are implemented to
preserve the population diversity. Orientating constrained engineering optimizati Platform: |
Size: 927744 |
Author:吴胜亮 |
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Description: The rst paper in the volume, Stochastic Evolution Equations by N. V. Krylov
and B. L. Rozovskii, was originally published in Russian in 1979 (Itogi Nauki i
Tekhniki, Seriya Sovremennye Problemy Matematiki, Vol, 14, pp. 71{146). The
English translation was rst published in the Journal of Soviet Mathematics, Vol.
14, pp. 1233{1277, 1981,
c Plenum Publishing Co. We are very grateful to the
current copyright holder, Springer, for the permission to include the paper in the
volume. After more than a quarter-century, this paper remains a standard reference
in the eld of stochastic partial di erential equations (SPDEs) and continues to
attract attention of mathematicians of all generations, because, together with a
short but thorough introduction to SPDEs, it presents a number of optimal and
essentially non-improvable results about solvability for a large class of both linear
and non-linear equations. Platform: |
Size: 1959936 |
Author:imedp |
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Search - Differential Evolution