A new multi objective optimization approach in distribution systems

This paper presents a multi objective optimal location of AVRs in distribution systems at the presence of distributed generators based on modified teaching-learning-based optimization (MTLBO) algorithm. In the proposed MTLBO algorithm, teacher and learner phases are modified. The proposed objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm utilizes several teachers and considers the teachers as an external repository to save found Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by trade-off) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as GA, PSO and TLBO, is extraordinary.     

A genetic algorithm based augmented Lagrangian method for constrained optimization

Among the penalty based approaches for constrained optimization, augmented Lagrangian (AL) methods are better in at least three ways: (i) they have theoretical convergence properties, (ii) they distort the original objective function minimally, thereby providing a better function landscape for search, and (iii) they can result in computing optimal Lagrange multiplier for each constraint as a by-product. Instead of keeping a constant penalty parameter throughout the optimization process, these algorithms update the parameters (called multipliers) adaptively so that the corresponding penalized function dynamically changes its optimum from the unconstrained minimum point to the constrained minimum point with iterations. However, the flip side of these algorithms is that the overall algorithm requires a serial application of a number of unconstrained optimization tasks, a process that is usually time-consuming and tend to be computationally expensive. In this paper, we devise a genetic algorithm based parameter update strategy to a particular AL method. The proposed strategy updates critical parameters in an adaptive manner based on population statistics. Occasionally, a classical optimization method is used to improve the GA-obtained solution, thereby providing the resulting hybrid procedure its theoretical convergence property. The GAAL method is applied to a number of constrained test problems taken from the evolutionary algorithms (EAs) literature. The number of function evaluations required by GAAL in most problems is found to be smaller than that needed by a number of existing evolutionary based constraint handling methods. GAAL method is found to be accurate, computationally fast, and reliable over multiple runs. Besides solving the problems, the proposed GAAL method is also able to find the optimal Lagrange multiplier associated with each constraint for the test problems as an added benefit??a matter that is important for a sensitivity analysis of the obtained optimized solution, but has not yet been paid adequate attention in the past evolutionary constrained optimization studies.     

An iterative algorithm to determine the number of time steps in path generation methods

The construction of Brownian motion paths is the most important part of simulation methods for option pricing. Particularly, there are several commonly used path generation methods in the context of quasi‐Monte Carlo, including the standard method and the Brownian bridge method. To apply each method, an inevitable step is to decide how many points are used to discretize the time interval. This paper implements an iterative algorithm to select a suitable number of time steps by successively adding discretization nodes until a specific convergence criterion is met. Numerical results with this algorithm are presented in the valuation of Asian options. Copyright © 2016 John Wiley & Sons, Ltd.     

A Meta-heuristic with Orthogonal Experiment for the Set Covering Problem

This paper reports an evolutionary meta-heuristic incorporating fuzzy evaluation for some large-scale set covering problems originating from the public transport industry. First, five factors characterized by fuzzy membership functions are aggregated to evaluate the structure and generally the goodness of a column. This evaluation function is incorporated into a refined greedy algorithm to make column selection in the process of constructing a solution. Secondly, a self-evolving algorithm is designed to guide the constructing heuristic to build an initial solution and then improve it. In each generation an unfit portion of the working solution is removed. Broken solutions are repaired by the constructing heuristic until stopping conditions are reached. Orthogonal experimental design is used to set the system parameters efficiently, by making a small number of trials. Computational results are presented and compared with a mathematical programming method and a GA-based heuristic.     

Multi-objective algorithm for the design of prediction intervals for wind power forecasting model

A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width.     

Application of robust optimization to automated test assembly

In automated test assembly (ATA), 0-1 linear programming (0-1 LP) methods are applied to select questions (items) from an item bank to assemble an optimal test. The objective in this 0-1 LP optimization problem is to assemble a test that measures, in as precise a way as possible, the ability of candidates. Item response theory (IRT) is commonly applied to model the relationship between the responses of candidates and their ability level. Parameters that describe the characteristics of each item, such as difficulty level and the extent to which an item differentiates between more and less able test takers (discrimination) are estimated in the application of the IRT model. Unfortunately, since all parameters in IRT models have to be estimated, they do have a level of uncertainty to them. Some of the other parameters in the test assembly model, such as average response times, have been estimated with uncertainty as well. General 0-1 LP methods do not take this uncertainty into account, and overestimate the predicted level of measurement precision. In this paper, alternative robust optimization methods are applied. It is demonstrated how the Bertsimas and Sim method can be applied to take this uncertainty into account in ATA. The impact of applying this method is illustrated in two numerical examples. Implications are discussed, and some directions for future research are presented.     

基于正交试验法和数值仿真的环形降压槽结构优化

降压槽存在相互矛盾的阻力和粘损特性均受各结构参数的影响,合理的结构参数选择一直是降压槽设计的难点问题.环形降压槽结构优化设计涉及到多因素多水平问题,数值仿真和正交试验设计方法相结合可以有效地减少对实验的依赖和试验次数.对环形降压槽进行结构参数化建模,建立了幂律流体在降压槽环空流道流动的数值仿真模型,可完成不同结构降压槽性能的数值仿真分析;制定了降压槽3因素3水平的多指标正交试验,采用极差和方差分析方法对试验的结果进行了分析,并用综合平衡的方法确定最优的降压槽结构参数组合.最终得到了各结构参数对降压槽的粘损和阻力性能的影响规律以及最优的结构参数,可为环形降压槽的设计和改进提供有价值的参考.     

POT模型中GPD估计方法选择及金融风险测度

极大似然、矩、概率加权矩估计是GPD参数估计的主要方法.用蒙特卡罗模拟技术产生大量GPD数据,用三种方法估计参数.通过BIAS和RMSE判断估计方法优劣,研究其随样本量和形状参数变化的差异,得到估计方法选择的标准.     

Fully homomorphic SIMD operations

At PKC 2010 Smart and Vercauteren presented a variant of Gentry’s fully homomorphic public key encryption scheme and mentioned that the scheme could support SIMD style operations. The slow key generation process of the Smart–Vercauteren system was then addressed in a paper by Gentry and Halevi, but their key generation method appears to exclude the SIMD style operation alluded to by Smart and Vercauteren. In this paper, we show how to select parameters to enable such SIMD operations. As such, we obtain a somewhat homomorphic scheme supporting both SIMD operations and operations on large finite fields of characteristic two. This somewhat homomorphic scheme can be made fully homomorphic in a naive way by recrypting all data elements separately. However, we show that the SIMD operations can be used to perform the recrypt procedure in parallel, resulting in a substantial speed-up. Finally, we demonstrate how such SIMD operations can be used to perform various tasks by studying two use cases: implementing AES homomorphically and encrypted database lookup.     

Multi-objective combinatorial optimization for selecting best available techniques (BAT) in the industrial sector: the COMBAT tool

The introduction of best available techniques (BAT) with the European Commission's directive 96/61, created a new framework for ‘cleaner’ production in the industrial sector. BATs practically constitute recommended techniques for each of the steps in the manufacturing process. Thus, the industries must decide on which BATs are most appropriate for their processes. In the current study, an integrated approach is applied in order to find the mixture of BATs for the entire industrial sector that satisfies as much as possible the economic and the environmental criteria. The former represent the industry owner's point of view expressed by the Net Present Value of the projects and the latter represent the society's point of view quantified by the emission reduction in some major pollutants. The developed multi-objective optimization model is addressed using two methods: (1) goal programming and (2) generation of the Pareto optimal solutions using an augmented version of the ε-constraint method followed by an interactive filtering process in order to select the most preferred Pareto optimal solution. The generation of the Pareto optimal solutions is performed using an improved version of the widely used ε-constraint method that overcomes some of its known drawbacks. The COMBAT tool (combinatorial optimization with multiple criteria for BAT selection) that is developed for implementing these methods is also described and the results from its application in the industrial sector of the greater Athens area are presented.