Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.     

基于布谷鸟算法的工业机器人轨迹跟踪控制

研究SCARA工业机器人在关节空间内的轨迹跟踪控制问题.实际应用中,系统的未建模特性、关节摩擦间隙和未知负载等因素将引起机器人动力学性能的变化,从而影响其轨迹跟踪控制;并且外界扰动也会增加机器人轨迹跟踪控制的难度.针对上述问题,提出一种基于布谷鸟算法优化的快速连续非奇异终端滑模控制策略.该方法利用布谷鸟算法寻优机制规划...     

Sandbox modeling of fault formation and evolution in the Weixinan Sag, Beibuwan Basin, China

Fault formation and evolution in the presence of multiple pre-existing weaknesses has not been investigated extensively in rift basins.The fault systems of Weixinan Sag,Beibuwan Basin of China,which is...     

大型复杂跨流域调水工程电站-水库-泵站群多目标优化调度

考虑调水量、发电最大和耗能最小3个目标,建立电站-水库-泵站群多目标优化调度模型。基于参数调整策略、邻域变异和加速策略,提出了求解多目标优化调度模型的综合改进布谷鸟新算法并求解模型,获得了发电、调水、泵站耗能多目标Pareto解集。以引汉济渭大型复杂跨流域调水工程为实例,将该模型与模拟调度模型和NSGA-Ⅱ算法多目标优化调度模型的结果进行比较,结果表明,该优化调度模型的发电、调水、耗能、弃水等各项指标合理,具有相对优势。     

高邮凹陷真武断裂构造演化特征

真武断裂是苏北盆地高邮凹陷重要的基底断裂,研究其演化规律对认识高邮凹陷的构造演化特征至关重要。在断裂构造解释的基础上,根据区域应力场状态将真武断裂的构造演化分为挤压、反转和伸展共3个主要时期。挤压期,真武断裂为印支期板块碰撞所形成的逆冲断裂;反转期,真武断裂继承性演化为真1断层和吴1断层,二者的构造演化特征存在差异;伸展期,受真1断层和吴1断层影响,形成真2断层和吴2断层等一系列次级断层,共同组成了真武断裂带和吴堡断裂带。高邮凹陷真武断裂伸展期砂箱模拟实验结果表明,区域拉张应力场和作为基底断裂的真武断裂是高邮凹陷南部边界断裂带构造演化的2个重要因素。     

混合CS算法的DE算法

为解决基本差分进化算法的缺陷,利用布谷鸟搜索（CS）算法寻优能力强的优点,在DE每次完成选择操作后,不直接进入下一次迭代,而是引入CS算法,继续进行搜索,这样就增加了粒子的搜索活力,从而得到一种新的差分进化算法。经过对6个标准测试函数的大量实验计算表明,该算法能有效克服DE算法的缺陷,使寻优精度有较大改进。将算法应用于求解非线性方程组问题,给出了数值算例。     

Optimal Location of Unified Power Quality Conditioner in Distribution System for Power Quality Improvement

The increased use of nonlinear loads in distribution system is increasing the distortion in the voltage and current waveforms. Moreover, the distribution systems are inherently unbalanced. This paper presents Cuckoo Optimization Algorithm (COA) based Unified Power Quality Conditioner (UPQC) allocation in three phase unbalanced distribution network. The performance of UPQC is studied in terms of minimization of load disturbance during fault condition in the test systems, % reduction of total harmonic distortion and individual harmonics, minimization of real power loss, decrease in voltage unbalance and increase in cost savings during normal operating condition. The performance of COA is compared with other soft computing techniques to get the better results, i.e., better voltage profile, the optimal location and optimal number of UPQCs.     

Optimal Power System Stabilizers design via Cuckoo Search algorithm

Cuckoo Search (CS) algorithm is introduced in this paper for optimal Power System Stabilizers (PSSs) design in a multimachine power system. The PSSs parameter tuning problem is formulated as an optimization problem which is solved by CS Algorithm. An eigenvalues based objective function involving the damping ratio, and the damping factor of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed CS based PSSs (CSPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under different operating conditions and disturbances. The results of the developed CSPSS are verified through time domain analysis, eigenvalues and performance indices. Also, the effectiveness of the proposed algorithm in providing good damping characteristics is confirmed.