A hybrid differential evolution algorithm for meta-task scheduling in grids

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

Greedy Constructive Procedure-Based Hybrid Differential Algorithm for Flexible Flow shop Group Scheduling

Aiming at the flexible flowshop group scheduling problem,taking sequence dependent setup time and machine skipping into account, a mathematical model for minimizing makespan is established,and a hybrid differential evolution( HDE) algorithm based on greedy constructive procedure( GCP) is proposed,which combines differential evolution( DE) with tabu search( TS). DE is applied to generating the elite individuals of population,while TS is used for finding the optimal value by making perturbation in selected elite individuals. A lower bounding technique is developed to evaluate the quality of proposed algorithm. Experimental results verify the effectiveness and feasibility of proposed algorithm.

Hybrid Improved Self-adaptive Differential Evolution and Nelder-Mead Simplex Method for Solving Constrained Real-Parameters

In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE （differential evolution） algorithm and exploitation capability of Nelder-Mead simplex method is presented （HISADE-NMS）. The DE has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles （NP）, scaling factor （F） and crossover control （CR）, which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depends on the characteristics of each objective function, therefore, we have to tune their value in each problem that mean it will take too long time to perform. In the new manner, we present a new version of the DE algorithm for obtaining self-adaptive control parameter settings. Some modifications are imposed on DE to improve its capability and efficiency while being hybridized with Nelder-Mead simplex method. To valid the robustness of new hybrid algorithm, we apply it to solve some examples of structural optimization constraints.

Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm

Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.

A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.

Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

多主体博弈下基于改进灰狼算法的综合能源服务商优化运行策略

为促进新能源消纳和满足不同负荷需求,本文提出了多主体博弈下的综合能源服务商优化运行策略。通过分析不同主体的交互及利益关系,建立综合能源服务商与分布式发电商、负荷用户的主从博弈双层优化模型,上层综合能源服务商通过售能价格激励下层负荷用户调整用能需求,并以高于新能源上网电价的购电价格激励下层分布式发电商进行售电量调整。针对模型的高维、非线性特征,通过引入差分进化和精英保留策略加速灰狼算法的全局搜索能力和收敛性。仿真结果表明所建模型在兼顾不同主体利益的同时,降低了系统的碳排放量,所提优化求解方法快速高效。

基于差分进化改进混合核极限学习机的指纹定位

针对极限学习机(extreme learning machine,ELM)指纹定位泛化性能弱、鲁棒性差等问题,提出一种改进的差分进化算法优化混合核极限学习机的指纹定位方法。该方法利用改进型的Logistic混沌映射提高差分进化算法全局搜索的能力,同时利用动态控制参数法避免差分进化算法陷入局部最优,然后通过改进差分进化算法自适应调整混合核极限学习机的参数,提高训练效率。在线阶段,利用混合核函数提高极限学习机的学习性能和泛化性能,并引入L1惩罚函数防止过拟合。其泛化能力相较于单一核极限学习机提升明显。该方法有92%的测试点定位误差小于0.5 m,平均误差相较于加权K近邻法(weighted Knearest neighbor,WKNN)降低了32.6%。

基于模糊双反馈的高速列车主动控制策略

为提高高速列车主动悬挂控制系统的精度,提出了一种基于差分进化(DE)改进的灰狼算法(GWO)的高速列车主动悬挂模糊双反馈主动控制策略。该策略基于高速列车主动悬挂系统垂向六自由度模型,采用了具有双反馈机制的模糊PID控制器,并用DE-GWO算法调整PID参数,该算法通过采用差分进化机制防止算法过早收敛。将采用改进灰狼算法优化后的模糊双反馈PID控制器与被动悬挂系统、PID控制器和优化前的模糊PID控制器进行比较,结果显示该控制策略使主动悬挂的加速度均方根值优化了45.80%,垂向平稳性指标降低至1.515,平稳性提高了25.07%。所提主动控制策略应用在高速列车主动悬挂系统时的垂向振动控制效果显著,可有效改善高速列车运行均衡性。

基于灰狼优化算法的阮桥闸底板尺寸优化设计

在保证水闸稳定的前提下,为了减小水闸底板的工程量,对底板尺寸进行优化设计。选取阮桥闸实际工程作为分析对象,对底板尺寸进行优化设计。首先,建立以底板截面面积为目标函数,底板抗滑稳定、底板基底应力为约束条件的水闸底板尺寸优化数学模型;然后,分别利用灰狼优化算法与差分进化算法对水闸底板尺寸优化数学模型进行优化求解;最后,将灰狼优化算法得到的结果与用差分进化算法得到的优化结果进行比较。结果表明,灰狼优化算法对于阮桥闸底板尺寸优化设计问题的优化效果更好。

灰狼优化与差分进化的混合算法及函数优化

灰狼优化(Grey Wolf Optimization,GWO)算法是近年被提出的一种新型智能优化算法,具有收敛速度快和优化精度高的特点,但对于一些复杂优化问题易陷入局部最优。差分进化(Differential Evolution,DE)算法的全局搜索能力强,但其性能对参数敏感,且局部搜索能力不足。为了发挥二者各自的优点并弥补存在的缺陷,提出了一种灰狼优化与差分进化的混合优化算法。首先使用嵌入趋优算子的GWO算法搜索,以便在更短的过程中获得更高的优化精度和更快的收敛速度;然后采用自适应调节参数的差分进化策略来进一步提高算法对复杂优化函数的寻优性能,从而获得一种高性能的混合优化算法,以便能更高效地解决各种函数优化问题。对12个高维函数的优化结果表明,与标准GWO,ACS,DMPSO及SinDE相比,新的混合优化算法不仅具有更好的收敛速度和优化性能,而且具有更好的普适性,更适用于解决各种函数优化问题。

基于差分进化算法和遗传算法的混合优化算法及其在阵列天线方向图综合中的应用

阵列天线方向图综合作为智能天线的一项重要技术对于复杂系统的优化问题提出了更高的要求 .本文结合差分进化的基本思想和遗传算法的基因交叉和变异方法构建了一种新的混合优化方法 .该算法使用差分进化算子作为主要优化手段 ,结合遗传算法优胜劣汰的基本思想以及其中的交叉和变异方法 ,通过融合两种优化方法各自的优点 ,可以显著改善多参数、高度非线性问题的优化结果、提高计算效率 .计算机仿真结果表明 ,与现有算法相比 ,该算法用于天线方向图综合具有收敛速度快、优化能力强。

基于混合差异进化优化算法的电力系统无功优化

无功优化是电力系统实现电压和无功功率最优控制和调度的基础,阐述了一种基于混合差异进化算法的新无功优化方法。混合差异进化算法是一种直接随机搜索方法,由在当前种群中随机采样的个体之间的基因差异来驱动,且为缩短计算时间、避免陷入局部最优,在算法中嵌入了加速操作和种群迁移操作。将该无功优化方法在IEEE 30节点系统上进行了校验,并与基于其他算法的无功优化方法进行比较,仿真结果表明该算法具有收敛速度快、鲁棒性好、计算精度高的优点。

基于粒子群和差分进化算法的含分布式电源配电网故障区段定位

配电网中引入分布式电源将导致传统的故障区段定位方法不再适用。通过构建新的开关函数,提出了基于二进制混合算法的配电网故障区段定位方法。该方法可动态适应分布式电源的投切,在进行多重故障定位时只需确定一次正方向,利用双种群进化策略和信息交换机制实现了粒子群和差分进化算法的混合。算例分析结果表明该方法能够对含分布式电源的配电网中的单一和多重故障进行准确定位,并且具有一定的容错性和高效性。

一种改进的灰狼优化算法

灰狼优化算法是最近提出的一种较有竞争力的优化技术.然而,它的位置更新方程存在开发能力强而探索能力弱的缺点.受差分进化和粒子群优化算法的启发,构建一个修改的个体位置更新方程以增强算法的探索能力;受粒子群优化算法的启发,提出一种控制参数a随机动态调整策略.此外,为了提高算法的全局收敛速度,用混沌初始化方法产生初始种群.采用18个高维测试函数进行仿真实验,结果表明:对于绝大多数情形,在相同最大适应度函数评价次数下,本文算法的性能明显优于标准灰狼优化算法.

基于改进PSO和DE的混合算法

研究粒子群优化(PSO)算法和差分进化(DE)算法的优缺点,通过改进PSO算法并与DE算法混合,得到一种双种群的新型混合全局优化算法。经过对5个标准测试函数的大量实验计算表明,该算法能有效克服PSO算法和DE算法的缺陷,使寻优精度有较大改进,在高维情况下表现更加突出。

基于混合差分进化算法的地球物理线性反演

地球物理反问题线性化处理之后,各种反演算法归结为对病态线性方程组的求解.为了快速准确地计算出地球物理参数,本文提出了一种全新的基于LSQR算法的混合差分进化算法(Hybrid Differential EvolutionAlgorithm,HDE).该算法利用LSQR算法给出DE算法的初始种群,提高DE算法的计算速度和稳定性.在不同噪声水平下,对四种正则化方法Tikhonov、TSVD、LSQR和HDE的反演结果进行详细比较.理论模型和实际数据反演的结果都表明:改进的HDE算法应用于地球物理反问题的求解是成功的:反演结果与原设定模型具有较高的相关性,在稳定性和准确性上较常规的反演算法都具有一定的优势;而且不需要给定正则化参数,具有更强的实用性.

一种基于粒子群优化算法和差分进化算法的新型混合全局优化算法

提出一种基于粒子群算法(PSO)和差分进化算法(DE)相结合的新型混合全局优化算法——PSODE.该算法基于一种双种群进化策略,一个种群中的个体由粒子群算法进化而来,另一种群的个体由差分操作进化而来.此外,通过采用一种信息分享机制,在算法执行过程中两个种群中的个体可以实现协同进化.为了进一步提高PSODE算法的性能,摆脱陷入局部最优点,还采用了一种变异机制.通过4个标准测试函数的测试并与PSO和DE算法进行比较,证明本文提出的PSODE算法是一种收敛速度快、求解精度高、鲁棒性较强的全局优化算法.

一种基于差分进化和灰狼算法的混合优化算法

针对差分进化易陷入局部最优和灰狼算法易早熟停滞的缺点,提出了一种基于差分进化(DE)算法和灰狼(GWO)算法的混合优化算法(DEGWO)。该算法利用差分进化的变异、选择算子维持种群的多样性,然后引入灰狼算法与差分进化的交叉、选择算子进行全局搜索。在整个寻优过程中,反复迭代渐进收敛。选取此3个测试函数进行仿真验证,结果表明,混合优化算法相比于DE算法和GWO算法,其求解精度、收敛速度、搜索能力都有了显著提高。

基于混合算法的配电网故障重构与孤岛运行配合

基于二进制粒子群和差分进化的混合算法,构建了故障恢复的数学模型,对重构、拓扑识别、孤岛划分、潮流计算和目标函数计算的实现进行了详细描述;并提出一种能使配电网重构和孤岛划分合理配合的恢复策略,即在重构过程中进行孤岛划分,通过建立孤岛界限矩阵判断重构时能否产生孤岛,将孤岛划分后得到的停电负荷作为目标函数的一部分,使重构和孤岛划分的结果共同影响全局最优解,从而使这2种恢复方式更具有整体性。以IEEE 33节点配电系统作为算例验证了算法的有效性。