Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Design and performance study of phase‐locked loop using fractional‐order loop filter

The present work reports the realization of an analog fractional‐order phase‐locked loop (FPLL) using a fractional capacitor. The expressions for bandwidth, capture range, and lock range of the FPLL have been derived analytically and then compared with the experimental observations using LM565 IC. It has been observed that bandwidth and capture range can be extended by using FPLL. It has also been found that FPLL can provide faster response and lower phase error at the time of switching compared to its integer‐order counterpart. Copyright © 2014 John Wiley & Sons, Ltd.     

A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments.     

Experimental optimization of power-function-shaped drive pulse for stick-slip piezo actuators

Motion of a stick-slip piezo actuator is generally controlled by the parameters related to its mechanical design and characteristics of the driving pulses applied to piezoceramic shear plates. The goal of the proposed optimization method is to find the driving pulse parameters leading to the fastest and the most reliable actuator operation. In the paper the method is tested on a rotary stick-slip piezo actuating system utilized in an atomic force microscope.The optimization is based on the measurement of the actuator response to driving pulses of different shapes and repetition frequencies at various load forces. To provide it, a computer controlled testing system generating the driving pulses, and detecting and recording the corresponding angular motion response of the actuator by a position sensitive photo detector (PSPD) in real time has been developed. To better understand and interpret the experimental results, supportive methods based on a simple analytical model and numerical simulations were used as well.In this way the shapes of the single driving pulses and values of the load force providing the biggest actuator steps were determined. Generally, the maximal steps were achieved for such a combination of the pulse shapes and load forces providing high velocities at the end of the sticking mode of the actuator motion and, at the same time, lower decelerations during the slipping mode.As for the multiple driving pulses, the pulse shapes and values of repetition frequency ensuring the sticking mode of the actuator motion during the pulse rise time together with the maximum average angular rotor velocity were specified. In this way the effective and stable operation conditions of the actuator were provided.In principle, the presented method can be applied for the testing and optimization of any linear or angular stick-slip actuator.     

NSGA-Ⅱ和NSGA-Ⅲ应用于换热网络多目标优化的对比

针对换热网络多目标优化问题，采用目前应用较广泛的两种多目标遗传算法，即NSGA-Ⅱ和NSGA-Ⅲ，对两种算法的性能进行对比研究。案例研究结果表明，NSGA-Ⅱ算法比NSGA-Ⅲ算法运行效率更高，NSGA-Ⅲ的运行时间是NSGA-Ⅱ的2倍以上；NSGA-Ⅱ算法的应用并不严格地受限于3个目标的最大目标数量，NSGA-Ⅱ在求解大于3个目标的多目标优化问题时也可能具有良好的性能，目标数量并非选择NSGA-Ⅱ和NSGA-Ⅲ算法的严格标准。对10×5换热网络案例进行4个相关目标改造优化时，从换热网络的单一性能指标来看，NSGA-Ⅱ算法更容易获得各目标的极值。从最小年度总费用的评价指标来看，两种算法的最优方案效果相近。对7×3换热网络案例进行6个目标的优化时，NSGA-Ⅲ算法得到各目标的极值较优。从最小年度总费用的评价指标来看，NSGA-Ⅲ算法获得的投资费用和年度总费用更小。对于目标函数数量不超过3个，或者3个以上具有一定相关性的多目标优化问题，推荐优先使用NSGA-Ⅱ算法实现快速寻优；而NSGA-Ⅲ算法由于其基于参考点的选择机制，运行效率较慢，更适合用于收敛困难的高维多目标优化问题。     

优化军工科研项目管理的具体策略

我国的军工科研所成立于上世纪，它以军工项目的科研、预研为主，为我国的国防军工提供研发产品。伴随着我国经济实力的增强和科技军事力量的增长，军工科研院所也转变了原有的工业专属性质，走上了军民两用、寓军于民的道路，并在时代的进步中成为了我国先进装备制造业的重要科研力量和科技创新支柱。凸现军事工业“高、精、尖”的特点，以项目管理的优化为具体策略进行军工科研开发工作。     

The robust knapsack problem with queries

We consider robust knapsack problems where item weights are uncertain. We are allowed to query an item to find its exact weight,where the number of such queries is bounded by a given parameter Q. After these queries are made, we need to pack the items robustly, i.e., so that the choice of items is feasible for every remaining possible scenario of item weights.The central question that we consider is: Which items should be queried in order to gain maximum profit? We introduce the notion of query competitiveness for strict robustness to evaluate the quality of an algorithm for this problem, and obtain lower and upper bounds on this competitiveness for interval-based uncertainty. Similar to the study of online algorithms, we study the competitiveness under different frameworks, namely we analyze the worst-case query competitiveness for deterministic algorithms, the expected query competitiveness for randomized algorithms and the average case competitiveness for known distributions of the uncertain input data. We derive theoretical bounds for these different frameworks and evaluate them experimentally. We also extend this approach to Γ-restricted uncertainties introduced by Bertsimas and Sim.Furthermore, we present heuristic algorithms for the problem. In computational experiments considering both the interval-based and the Γ-restricted uncertainty, we evaluate their empirical performance. While the usage of a Γ-restricted uncertainty improves the nominal performance of a solution (as expected), we find that the query competitiveness gets worse.     

基于SVM的煤与瓦斯突出预测模型及应用

为有效预测矿井内煤与瓦斯突出的危险程度,对其影响因素做了分析与探讨,分别构建了基于粒子群优化算法以及遗传算法支持向量机的煤与瓦斯突出预测模型,并且通过实例对两种模型预测的准确性进行了验证。分别利用单项以及综合指标、BP神经网络以及PSO-SVM模型、GA-SVM模型,对寺河煤矿二号井的突出区域进行预测比较。结果表明,PSO-SVM的预测模型不仅可以在小样本数据中预测出煤与瓦斯突出程度的大小,而且综合预测结果更加精确,其在解决矿井内煤与瓦斯突出的小样本数据中显示出更加强大、通用的性能。     

Collapse load of composite laminates: Lower bound evaluation by stress field analytical approximation

The paper proposes a limit analysis approach to define the ultimate load capacity of orthotropic composite laminates under biaxial loading and plane stress conditions. A lower bound to the collapse load multiplier is computed by solving a maximization nonlinear problem, according to the static theorem of limit analysis. To set up the optimization problem a stress field distribution is hypothesized at lamina level, moreover inter-lamina stresses are also considered. The effectiveness and validity of the proposed approach is shown by comparing the obtained numerical predictions both with available experimental data and with other numerical results carried out by means of a different numerical lower bound approach.