A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.     

Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms

We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems.     

Analyzing IT maintenance outsourcing decision from a knowledge management perspective

IT outsourcing is a complex and opaque decision problem. Managers facing a decision about IT outsourcing have difficulty in framing what needs to be thought about further in their discourses. Framing is one of the most crucial steps of human decision making and needs to be assisted to better understand a decision situation. In this research, we examine a number of decision primitives in the context of an IT outsourcing decision situation. We demonstrate how the decision primitives can be employed so that managers can probe deep to better understand a decision situation and to establish a decision basis. In the organizational setting, we exemplify the use of the decision primitives in relation to the perceived outsourcing implications for the managers looking for assistance in accommodating a knowledge management perspective on IT outsourcing. Consequently, we induce insight and a guideline on how to use knowledge management for effective outsourcing in one of the leading financial institutes in Europe.

An incremental neural network for tissue segmentation in ultrasound images

This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images.     

Coordinating metaheuristic agents with swarm intelligence

Coordination of multi agent systems remains as a problem since there is no prominent method suggests any universal solution. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems using metaheuristic algorithms. An idea for coordinating metaheuristic agents borrowed from swarm intelligence is introduced in this paper. This swarm intelligence-based coordination framework has been implemented as swarms of simulated annealing agents collaborated with particle swarm optimization for multidimensional knapsack problem. A comparative performance analysis is also reported highlighting that the implementation has produced much better results than the previous works.     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Towards quality requirements via animated formal specifications

Assuring a high quality requirements specification document involves both an early validation process and an increased level of participation. An approach and its supporting environment which combines the benefits of a formal system specification and its subsequent execution via a rapid prototype is reported. The environment assists in the construction, clarification, validation and visualisation of a formal specification. An illustrative case study demonstrates the consequences of assertions about system properties at this early stage of software development. Our approach involves the pragmatic combination of technical benefits of formal systems engineering based techniques with the context‐sensitive notions of increased participation of both developer and user stakeholders to move us closer towards a quality requirements specification document.     

A numerical analysis of sheet metal formability for automotive stamping applications

A theoretical failure model is presented for the numerical prediction of the forming limit strains of automotive sheets. The model uses the Swift’s diffuse necking and Hill’s localized necking concepts in describing tearing-type sheet metal failures and a computational scheme is proposed in which the failure conditions are expressed in incremental forms. The Bauschinger effect is included properly in the deformation modeling using an additive backstress form of the nonlinear-kinematic hardening rule. The necking conditions and plasticity model are transformed into a set of algebraic equations that may be applied both for proportional and non-proportional strain-controlled loadings. An iterative approach is employed in the incremental solution of algebraic equations. The formability analyses are conducted using the proposed theoretical model and the forming limit strains of two new generation auto sheets (Trip600 1.4 mm, DP980 1.15 mm) are estimated. The numerically generated FLC are compared with the experimental data and the FLC calculated with the Keeler–Brazier equation. For both steels, the model produced conservative plain–strain intercept values, FLC₀, when compared with the predictions of Keeler–Brazier equation. Also the negative minor strain part of the experimental FLD’s is estimated with sufficient accuracy. For the positive minor strain side, however, the predictions are lower than both the experimental fit and the standard curve.     

Realization of reconfigurable filtering horn antennas using active frequency selective surfaces for GSM and LTE signal filtering

Herein, it is aimed to design an Active Frequency Selective Surface (AFSS) based high performance reconfigurable filtering antenna (Filtenna). The proposed AFSS unit element is consist of a single PIN diode with a simple microstrip patch design which performance characteristics are varies with the state of the diodes. The proposed unit elements have been used to form an array that is placed at the aperture of a double ridge horn antenna. With the variation in state of diodes, the antenna acts as a Filtenna module to filtering the incoming electromagnetic waves in frequency band of 1.8 to 2.8 GHz. From the experimental results, it is shown that the proposed Filtenna achieves a signal attenuation of 7.3 dBi at 2.1 GHz.     

低噪声APD偏置电路

１ＡＰＤ的特性雪崩光电检测器 (ＡＰＤ)和ＰＩＮ二极管通常被作为接收器用于光通信中。其中ＡＰＤ适合于高灵敏度和高带宽的光接收电路。但这种器件在工作时需要施加一个反向结压 ,这样 ,当接收到射线时产生的电子空穴对会被外加电场收集并转换为电流 ,其电流强度正比于射线强度。另外 ,工作时施加在器件上的反向偏置电压会引发雪崩效应 ,其雪崩增益可通过改变偏压来进行调节。这就有可能对光纤接收器的增益进行优化。然而 ,要得到满意的雪崩增益 ,就必须给ＡＰＤ提供一个比较高的反向偏压。很多ＡＰＤ需要４０Ｖ～６０Ｖ的偏压 ,有些器件…