Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Improved Adaptive Differential Evolution Algorithm for the Un-Capacitated Facility Location Problem

The differential evolution algorithm is an evolutionary algorithm for global optimization and the un-capacitated facility location problem (UFL) is one of the classic NP-Hard problems. In this paper, combined with the specific characteristics of the UFL problem, we introduce the activation function to the algorithm for solving UFL problem and name it improved adaptive differential evolution algorithm (IADEA). Next, to improve the efficiency of the algorithm and to alleviate the problem of being stuck in a local optimum, an adaptive operator was added. To test the improvement of our algorithm, we compare the IADEA with the basic differential evolution algorithm by solving typical instances of UFL problem respectively. Moreover, to compare with other heuristic algorithm, we use the hybrid ant colony algorithm to solve the same instances. The computational results show that IADEA improves the performance of the basic DE and it outperforms the hybrid ant colony algorithm.

Multi-robot mapping based on the adaptive differential evolution

Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building.A novel approach is put forward based on adaptive differential evolution to map building for the multi-robot system.The multi-robot mapping-building system adopts the methods of decentralized exploration and concentrated mapping.The adaptive differential evolution algorithm is used to search in the space of possible transformation,and the iterative search is performed with the goal of maximizing overlapping regions.The map is translated and rotated so that the two maps can be overlapped and merged into a single global one successfully.This approach for map building can be realized without any knowledge of their relative positions.Experimental results show that the approach is effective and feasibile.

A Hybrid Differential Evolution Algorithm Integrated with Particle Swarm Optimization

To implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization （PSODE） is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution （ DE） operators are used to evolve the original population. And, particle swarm optimization （PSO） is applied to co-evolving the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functious. The results show that the average performance of PSODE is the best.

Covariance Matrix Learning Differential Evolution Algorithm Based on Correlation

Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which makes the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper proposes covariance matrix learning differential evolution algorithm based on correlation (denoted as RCLDE) to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally, the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is an effective algorithm.

基于PCA-SaDE-ELM优化算法的煤层底板破坏深度预测及工程应用

基于煤层底板破坏深度实测结果统计分析,通过优化数据样本空间,引入自适应差分进化改进的极限学习机算法,构建了煤层底板破坏深度预测模型,与实测结果对比分析验证,并应用于云驾岭煤矿9^(#)煤层底板破坏深度预测。结果表明:模型预测的最大绝对误差不超过0.7 m,相比现有其他预测模型,该模型预测精度提高约70%;云驾岭煤矿19101、19103和19105这3个典型工作面的破坏深度分别为10.80、10.94、11.34 m,介于规范方法和滑移场理论预测结果之间,进一步反映了模型的可靠性;建议对9#煤层底板加固改造后再进行回采。相关研究成果可为我国煤层底板破坏风险管理和煤炭资源的优化回采布置提供一定的理论支撑。

V2G模式下基于SaDE-BBO算法的有源配电网优化

为了解决大规模电动汽车入网难以实现个体调度以及集群调度存在“维数灾”的问题,建立基于车辆到电网(vehicle-to-grid,V2G)模式的有源配电网分层分区优化运行模型。其中,上层优化模型对电动汽车集控中心(electric vehicle agent,EVA)进行调度,优化各区域EVA的充放电功率并作为下层优化模型的输入;下层优化模型调整各调压方式。在优化算法方面,提出一种自适应差分进化-生物地理学优化(self-adaptive differential evolution-biogeography-based optimization,SaDE-BBO)算法,并在改进的IEEE 33节点配电系统中进行仿真分析。结果表明:在不同充电控制策略下,V2G模式与各调压方式的协调互动在降低各区域EVA运营成本、平抑负荷波动以及保证有源配电网的安全和经济运行方面优势显著,与其他优化算法相比,SaDE-BBO算法具有更优质的解和更好的收敛性。

Position detection of BLDC rotor based on adaptive wavelet neural network

Brushless DC(BLDC)motor is a complex nonlinear system,of which some parameters will also change during operation.Therefore,obtaining accurate rotor position directly through the line voltage becomes more difficult.So a new method is proposed in this paper which uses three line voltages as the input signal to identify the motor position based on adaptive wavelet neural network(WNN)and the differential evolution(DE)algorithm to optimize WNN structures,thus realizing the improvement of accuracy,exactness of the communication signals and convergence speed of the rotor position identification.Finally,both simulations and experimental results show that the proposed method has high accuracy of recognizing rotor position and strong orientation ability.

基于MSADE-IT2FNN模型的软测量建模方法及应用

针对复杂工业过程某些关键参数无法有效、实时在线检测的问题,提出一种基于多策略、自适应差分进化算法(MSADE)优化的区间二型模糊神经网络(IT2FNN)软测量建模方法。首先,为了解决差分进化算法采用单一策略、固定缩放因子和交叉概率导致后期搜索能力不足的问题,提出一种多策略、自适应的差分进化算法(MSADE),该算法利用IT2FNN模型的均方根误差(RMSE)作为适应度函数,通过搜索不同规则数下的RMSE值,从而确定IT2FNN的结构(规则数)和初始参数;然后,IT2FNN模型的参数利用梯度下降法进行学习。最后,将所提模型应用到Mackey-Glass混沌时间序列的预测和酿酒过程淀粉利用率的软测量建模问题中,仿真结果验证了提出方法的有效性和优越性。

改进SADE-EKF的永磁同步电机参数辨识

针对永磁同步电机(PMSM)参数辨识中扩展卡尔曼滤波(EKF)难以确定合适的系统噪声矩阵Q和量测噪声矩阵R的问题,提出了一种改进自适应差分进化算法(SADE)-EKF的PMSM参数辨识方法。首先分析了扩展卡尔曼滤波器的工作原理,建立了双线程辨识模型;然后通过改进差分进化算法(DE)的变异策略跳出局部最优,并设计了合适的适应度函数;最后,通过SADE算法对EKF的Q和R进行优化。实验结果表明,改进的SADE-EKF在辨识电机参数时比传统的EKF具有更好的收敛速度和辨识精度。

基于ADE优化的IPMSM全速域无传感器控制

为了实现内置式永磁同步电机(IPMSM)全速域的无传感器控制和切换速域的平滑过渡,提出了一种基于自适应差分进化(ADE)算法优化的复合控制方法。分别在零低速域、中高速域采用旋转高频电压注入法和滑模观测器法来对电机转速和转子位置进行估算,并在切换速域采用基于ADE算法的权重系数优化法来实现上述两种控制方法的平滑切换,从而实现IPMSM全速域无传感器控制。仿真结果表明:提出的复合控制方法能够实现电机全速域的无感控制和切换速域的平滑过渡,且具有良好的稳定性。

基于L-SHADE算法的AUV载体磁干扰参数辨识的数值模拟

采用自主水下航行器(Autonomous Underwater Vehicle,AUV)磁测平台可开展海洋地磁场测量、水下磁性目标探测和识别等工作,AUV磁测平台具有广阔的应用前景,但目前AUV载体磁干扰补偿技术研究尚不成熟,制约着水下航行器测磁精度。基于磁测平台抗磁干扰基本原理,提出一种基于线性种群规模缩减和成功历史的参数自适应差分进化(Success History-based Adaptive Differential Evolution with Linear Population Size Reduction,L-SHADE)算法的AUV载体磁干扰参数辨识的数值模拟方法。用磁偶极子和旋转椭球壳混合模型来等效模拟AUV载体磁干扰,通过模拟航行获得多组磁测数据,据此建立磁干扰参数辨识模型,并采用L-SHADE算法求解。通过数值模拟实验定量分析研究磁测平台测磁精度随磁传感器、平台姿态及航向等误差的传播规律。研究结果表明:当磁传感器测量精度为10 nT、姿态测量精度为0.01°、航向测量精度为0.1°时,测磁误差可小于100 nT。设计的AUV磁测平台抗干扰试验表明,地磁场总量最大相对误差为1.07%。

基于差分进化算法的瞬变电磁一维反演

实际采集的瞬变电磁数据包含电磁感应和激发极化效应,如何准确提取电阻率和极化率信息是电性源瞬变电磁数据处理的关键。首先,基于Cole⁃Cole复电阻率模型实现有限长电性源瞬变电磁法一维正演,在此基础上提出一种基于差分进化算法的电性源瞬变电磁一维反演方法。然后,在传统差分进化算法的基础上引入反向学习策略及控制参数自适应调节,加快反演的收敛速度,同时在目标函数中引入约束条件,构成最小构造反演,降低反演的多解性。最后,基于典型的三层地电模型和复杂多层模型进行理论模型测试,反演结果可有效恢复模型的电阻率和极化率。利用实测资料进行反演,反演得到的电阻率与OCCAM反演电阻率基本一致。在此电阻率约束的基础上,进一步反演得到极化率信息。反演结果准确地提取了实测数据中的电阻率信息,得到了地下介质的极化率分布,证明了算法的准确性和适用性。

基于改进差分进化算法的跨平台武器目标分配方法

现代战争中,跨平台武器单元的协同利用,是合同编队体系的重要内容,作战方式也正由平台级协同向着能力要素级协同转变,这对武器目标分配问题的解决提出了更大挑战。本文将武器单元的最小划分单位细化到能力要素级,以毁伤概率与成本消耗为优化目标,面向多种来袭目标的编队防空场景,提出了跨平台武器目标分配算法。同时,基于混沌映射提出了混沌种群重构(chaotic population reconstruction,CPR)机制,并结合带存档的自适应差分进化(adaptive differential evolution with optional external archive,JADE)算法提出了CPR-JADE算法,利用CPR机制可以帮助算法在解决高维复杂约束问题时跳出局部最优。再将其运用到武器目标分配模型上,实现了对模型的高效求解。最后,通过在多种数据规模下与其他进化优化算法的仿真对比试验分析,验证了所提方法的正确性与有效性。

基于改进差分进化算法的自由曲面测量路径优化

为解决传统差分进化算法存在收敛速度慢、易陷入局部最优解以及由于个体选择的随机性导致求优稳定性差的问题,文章通过引入多重启动策略,多次运行算法并使用不同的随机种子,增加算法对空间的探索性,在一定程度上解决算法易陷入局部最优解问题;通过使用新的突变策略,在求优稳定性提高了约10%;通过引入参数自适应调节机制,动态地调整算法参数的取值,使收敛速度提高了约10%,并提高了算法的鲁棒性。

基于改进的差分进化算法求解SWMP问题

需求侧响应可以为电网带来可观的收益,计及需求响应的电力系统经济调度优化方法研究成为国内外学者的关注的课题。提出了一种可以用于解决计及需求侧响应的电力系统经济调度问题的基于随机黑洞模型的差分进化算法(sequence-based differential evolution hole,S-DEH),该算法基于原始的差分进化算法结合随机黑洞模型,对初始序列敏感性高,稳定可靠,计算量小,能够针对不同的问题选择最佳的控制参数,能够减少人为的操作失误和调整优化算法参数所需要的时间,具有很高的求解效率。同时所采用的演算模型为计及需求侧响应的求解电力系统的单时段的社会效益最大化问题(social welfare maximization problem,SWMP),以验证算法的性能,并通过求解多时段的SWMP优化调度问题验证了算法在处理高维度复杂优化问题时仍能保持高效的性能。

基于改进ADE的城轨列车运行节能优化方法

城轨列车运行环境复杂,模式多变。为准确描述列车的实际运行状态,首先基于实际线路条件建立城轨列车动力学模型和多目标优化模型。然后,基于反三角函数logistic映射的初始化改进策略和基于logistic模型的控制参数自适应策略,提出一种改进自适应差分进化(adaptive differential evolution,ADE)算法求解优化模型,可实现列车安全、准点、精确停车,节能、平稳运行。与传统高斯模型相比,通过采用钟形模型构建新的舒适度指标,能更好地改善舒适度。还针对多站间运行普遍采用固定运行策略的问题,结合专家经验和实际线路条件来自动选择站间运行策略,可缩小算法搜索范围并提高算法求解效率。最后,基于实际线路数据和车辆数据的仿真实验结果表明,所提方法有效降低了运行能耗,改善了乘客舒适性。

一种基于差异演化算法求解折扣{0-1}背包问题的应用研究

折扣{0-1}背包问题(Discounted(0-1)Knapsack Problem,D{0-1}KP)是一个经典的组合优化问题。将差异演化算法中实数编码映射为整数编码,进一步完善约束条件,求得D{0-1}KP最优解,并使用动态规划算法对改进算法进行验证,验证结果表明,改进后的差异演化算法能够用于求解数据规模大的D{0-1}KP,并能够得到适应度最高的最优个体,即最优解。在验证之后对差异演化算法中两个重要参数进行测试取值,得出使用差异演化算法求解D{0-1}KP收敛速度快、稳定性好的参数取值范围。

HCO-ADE求解单点交叉口信号配时问题

为克服信号配时问题求解中单一约束优化方法的局限性,提出混合约束优化自适应差分进化算法(HCO-ADE)。以交叉口通行效率为目标建立信号配时优化模型,依据模型中约束条件被满足程度,采用外罚函数法和可行性准则法相结合的混合约束优化策略(HCOS)对约束条件进行处理,并提出饱和度偏好准则作为自适应差分进化算法(ADE)的选择策略,构建HCO-ADE。最后,以哈尔滨市四相位交叉口高峰时段5 min采集标段交通量数据为例,验证HCO-ADE。结果表明:提出的信号配时问题求解方法可行、有效,优于基于饱和度区间可能度的罚函数约束优化自适应差分进化算法(PFCO-ADE),所得信号配时方案较实测方案平均延误减少3.32%,通行能力提高2.16%,能够更好地提高信号配时方案稳定性。

基于非线性自适应比例因子的雪豹优化算法

针对雪豹优化算法在求解复杂优化问题时,存在全局勘探能力不足、寻优精度低等问题,提出一种改进的雪豹优化算法。首先,基于分段Logistic混沌映射初始化从而提高初始种群多样性;其次,引入非线性比例因子用于平衡算法的全局勘探能力和局部开发能力;然后,提出了一种差分变异策略,在第一次种群更新位置后,使用5个随机个体提高全局搜索能力和算法收敛能力,在第二次种群更新位置后,使用3个随机个体保证在求解过程的中后期也具有一定的全局勘探能力,尽可能避免陷入局部最优。通过在IEEE CEC2022基准函数测试集上测试,并与其他算法进行比较,结果表明所提出的算法在种群质量、求解精度以及算法稳定性上均有较大提升。最后将所提出的算法应用于工程优化,计算结果进一步证实了算法的强优化能力。