Mission Critical: Realizing the Promise of Enterprise Systems

Inference and Disputed Authorship: The Federalist

When solving constrained optimization problems by evolutionary algorithms, an important issue is how to balance constraints and objective function. This paper presents a new method to address the above issue. In our method, after generating an offspring for each parent in the population by making use of differential evolution (DE), the well-known feasibility rule is used to compare the offspring and its parent. Since the feasibility rule prefers constraints to objective function, the objective function information has been exploited as follows: if the offspring cannot survive into the next generation and if the objective function value of the offspring is better than that of the parent, then the offspring is stored into a predefined archive. Subsequently, the individuals in the archive are used to replace some individuals in the population according to a replacement mechanism. Moreover, a mutation strategy is proposed to help the population jump out of a local optimum in the infeasible region. Note that, in the replacement mechanism and the mutation strategy, the comparison of individuals is based on objective function. In addition, the information of objective function has also been utilized to generate offspring in DE. By the above processes, this paper achieves an effective balance between constraints and objective function in constrained evolutionary optimization. The performance of our method has been tested on two sets of benchmark test functions, namely, 24 test functions at IEEE CEC2006 and 18 test functions with 10-D and 30-D at IEEE CEC2010. The experimental results have demonstrated that our method shows better or at least competitive performance against other state-of-the-art methods. Furthermore, the advantage of our method increases with the increase of the number of decision variables.

Incorporating Objective Function Information Into the Feasibility Rule for Constrained Evolutionary Optimization

A novel differential search algorithm and applications for structure design

The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution, authorship verification, authorship profiling, stylochronometry, and adversarial stylometry. Discussions on datasets, features, experimental techniques, and recent approaches are provided. Further, a current research challenge lies in the inability of authorship analysis techniques to scale to a large number of authors with few text samples. Here, we perform an extensive performance analysis on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature. Finally, several remaining research challenges are discussed, along with descriptions of various open-source and commercial software that may be useful for stylometry subtasks.

Surveying Stylometry Techniques and Applications

Optimization of reverse logistics network of End of Life Vehicles under fuzzy supply: A case study for Istanbul Metropolitan Area

The accuracy of forecasting is an essential issue for decision makers in terms of energy planning. During the recent years, several techniques have been used for electricity consumption forecasting in order to accurately predict the future demand. Although there are several forecasting techniques, selection of the most appropriate one is of paramount importance. In this study, three different grey forecasting models are built and used for modeling and predicting yearly net electricity consumption in Turkey. Additionally, these three models are compared to find the best model by using performance criteria. The best approach, Nonhomogeneous Discrete Grey Model (NDGM), is employed to forecast electricity consumption from 2014 to 2030. In addition, a comparison is made with recent studies proving the grey model (GM) proposed by this study delivers better forecasting performance.

Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model

Purpose




The global warming, caused by the anthropogenic greenhouse gases, has been one of the major worldwide issues over the last decades. Among them, carbon dioxide (CO2) is the most important one and is responsible for more than the two-third of the greenhouse effect. Currently, greenhouse gas emissions and CO2 emissions – the root cause of the global warming – in particular are being examined closely in the fields of science and they also have been put on the agenda of the political leaders. The purpose of this paper is to predict the energy-related CO2 emissions through using different discrete grey models (DGMs) in Turkey and total Europe and Eurasia region.




Design/methodology/approach




The proposed DGMs will be applied to predict CO2 emissions in Turkey and total Europe and Eurasia region from 2015 to 2030 using data set between 1965 and 2014. In the first stage of the study, DGMs without rolling mechanism (RM) will be used. In the second stage, DGMs with RM are constructed where the length of the rolling horizons of the respected models is optimised.




Findings




In the first stage, estimated values show that non-homogeneous DGM is the best method to predict Turkey’s energy-related CO2 emissions whereas DGM is the best method to predict the energy-related CO2 emissions for total Europe and Eurasia region. According to the results in the second stage, NDGM with RM (k=26) is the best method for Turkey while optimised DGM with RM (k=4) delivers most reliable estimates for total Europe and Eurasia region.




Originality/value




This study illustrates the effect of different DGM approaches on the estimation performance for the Turkish energy-related CO2 emission data.

Ali Osman Kusakci

Papers

Optimization of reverse logistics network of End of Life Vehicles under fuzzy supply: A case study for Istanbul Metropolitan Area

Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model

Energy-related CO2 emission forecast for Turkey and Europe and Eurasia: A discrete grey model approach

The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach

An adaptive penalty based covariance matrix adaptation–evolution strategy