jMetal: A Java framework for multi-objective optimization

This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems. jMetal includes a number of classic and modern state-of-the-art optimizers, a wide set of benchmark problems, and a set of well-known quality indicators to assess the performance of the algorithms. The framework also provides support to carry out full experimental studies, which can be configured and executed by using jMetal’s graphical interface. Other features include the automatic generation of statistical information of the obtained results, and taking advantage of the current availability of multi-core processors to speed-up the running time of the experiments. In this work, we include two case studies to illustrate the use of jMetal in both solving a problem with a metaheuristic and designing and performing an experimental study.     

Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.     

二维彩色条码打印系统软件的设计与实现

介绍了利用VB设计并实现的一种二维彩色条码的打印系统。该彩色条码相对于传统的黑白条码在第三维增加了信息容量,实现了在条空横向宽度、条空纵向排列和条空颜色三方面传载信息,可得到远大于目前黑白条码的容量。文中对编码软件系统框架进行整体介绍,实现了条码码字集的编码入库,二维彩色条码字符的打印及条码模块属性设置等关键技术,并附有关键代码。通过对软件系统的测试,效果良好。表明该二维彩色条码编码系统实现了比二维条码更高的信息容量和信息密度,具有广泛的应用前景。     

Non-linear system identification and fault diagnosis using a new GUI interpretation tool

There are two major contributions which are described in this paper. Firstly, a new graphical user interfaces (GUI) interpretation tool for the non-linear frequency response functions (FRF) is proposed. The tool is developed by pv-wave package to provide better ways and user friendly of displaying, interpreting and analysing of non-linear systems with significant non-linear effects. Secondly, the application of this identification tool for fault detection, identifying in the civil engineering.To demonstrate the improved visualisation and interpretation of the multi-dimensional non-linear FRF and the application of the non-linear auto-regressive moving average model with exogenous input (NARMAX) methodology, two engineering applications have studied including a non-linear oscillator and fault diagnosis in civil engineering structures. The results obtained have shown that the interpretation of the higher-order FRF has been comprehensively studied and non-linear effects have been related back to the physical models of the systems.     

Fault tolerant embedded systems design by multi-objective optimization

The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).     

Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures

This paper proposes a multi-objective genetic algorithm (MOGA) for optimal placements of control devices and sensors in seismically excited civil structures through the integration of an implicit redundant representation genetic algorithm with a strength Pareto evolutionary algorithm 2. Not only are the total number and locations of control devices and sensors optimized, but dynamic responses of structures are also minimized as objective functions in the multi-objective formulation, i.e., both cost and seismic response control performance are simultaneously considered in structural control system design. The linear quadratic Gaussian control algorithm, hydraulic actuators and accelerometers are used for synthesis of active structural control systems on large civil structures. Three and twenty-story benchmark building structures are considered to demonstrate the performance of the proposed MOGA. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement of actuators and sensors in seismically excited large buildings such that the performance on dynamic responses is also satisfied.     

ACE在开发健壮可靠的C＋＋系统中的应用研究

开发健壮可靠的C 系统在实际应用中具有重要的意义。自适配通信环境(ACE)是一种面向对象(OO)的工具包，它实现了通信软件的许多基本的设计模式。文中概述了在当今软件开发中流行的ACE工具包的强大功能，分析了在开发C 应用系统时遇到的各种挑战，然后重点阐述了ACE在解决这些难题时发挥的重要作用：如何实现多线程的同步与互斥、事件多路分离，最后经过成功应用ACE的现有功能，创建出健壮可靠的C 系统，在实际生产中能够达到预期的效果。     

Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior.     

Solving a single-machine scheduling problem with maintenance,job deterioration and learning effect by simulated annealing

This paper presents a new nonlinear multi-objective mathematical model for a single-machine scheduling problem with three objectives: (1) minimizing the sum of the weighted jobs completion, (2) minimizing the sum of the weighted delay times, and (3) maximizing the sum of the job values in makespan. In addition, a number of constraints are incorporated in this presented model, such as repairing and maintenance periods, deterioration of jobs, and learning effect of the work process. Since this type of scheduling problem belongs to a class of NP-hard ones, its solution by common software packages is almost impossible, or at best very time consuming. Thus, a meta-heuristic algorithm based on simulated annealing (SA) is proposed to solve such a hard problem. At a final stage, the related results obtained by the proposed SA are compared with those results reported by the Lingo 8 software in order to demonstrate the efficiency and capability of our proposed SA algorithm.     

Dynamic test planning: a study in an industrial context

Testing accounts for a relevant part of the production cost of complex or critical software systems. Nevertheless, time and resources budgeted to testing are often underestimated with respect to the target quality goals. Test managers need engineering methods to perform appropriate choices in spending testing resources, so as to maximize the outcome. We present a method to dynamically allocate testing resources to software components minimizing the estimated number of residual defects and/or the estimated residual defect density. We discuss the application to a real-world critical system in the homeland security domain. We describe a support tool aimed at easing industrial technology transfer by hiding to practitioners the mathematical details of the method application.     

基于改进元胞多目标遗传算法的机床主轴优化

为了进一步提高元胞遗传算法在求解多目标优化问题时的收敛性和分布性。在多目标元胞遗传算法的基础上,引入了三维空间元胞,提出了三维元胞多目标遗传算法。采用多目标基准测试函数对该算法进行了测试,并将其与目前比较流行的几种多目标遗传算法进行对比。结果表明,此种算法在收敛性和分布性上取得了更好的效果。采用以上这几种算法分别对机床主轴多目标优化问题进行了求解,相比其他几种算法,改进的多目标元胞遗传算法得到了更优的结果,说明了改进的算法在求解此问题时行之有效。     

隐形多彩色条形码打印系统软件的设计与实现

介绍利用VB设计并实现的以稀土能量上转换材料为码元的一种隐形多彩色一维条码的打印系统。该多彩色条码的编码原理是当红外光源通过光学系统照射在上转换材料上时,稀土能量上转换材料会在紫外光、可见光、红外光等光谱频率范围内发光。任意被激发的荧光带都带着丰富的信息码,通过挖掘稀土材料上转换机制所潜在的巨大信息潜能,将被激发不同颜色的稀土材料作为条码中的条码元,利用数学组合方法,该彩色条码可得到远大于目前黑白条码的容量。文中对编码软件系统框架进行整体介绍,实现了条码字符的编码入库,条码字符的打印及条码模块属性设置等关键技术,并附有关键代码。通过对软件系统测试,效果良好。基于此打印软件的打印条码经印刷套印,得到以不同稀土能量上转换材料为码元的隐形条码,具有防伪能力强和信息容量高等特点,具有广泛的应用前景。     

Applying modified NSGA-II for bi-objective supply chain problem

This paper minimizes the value of total cost and bullwhip effect in a supply chain. The objectives have been achieved through developing a new multi-objective formulation for minimizing the total cost and minimizing the bullwhip effect of a two-echelon serial supply chain. A new crossover algorithm for a fuzzy variable and a new mutation algorithm have also been proposed while applying Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to the proposed problem. The formulated problem has been simulated by Matlab software and the results of the modified NSGA-II have been compared with those of original NSGA-II. It is found from the results that the modified NSGA-II algorithm performs better than the original NSGA-II algorithm since the minimum values for both total cost and the bullwhip effect are obtained in case of the modified NSGA-II. The formulated bi-objective problem is new to the research community. The minimization of bullwhip effect has never been considered in a multi-objective optimization before. Besides crossover operator applied to the fuzzy variable and the mutation operator are newly introduced operators.     

ADEMO/D: An adaptive differential evolution for protein structure prediction problem

Protein Structure Prediction (PSP) is the process of determining three-dimensional structures of proteins based on their sequence of amino acids. PSP is of great importance to medicine and biotechnology, e.g., to novel enzymes and drugs design, and one of the most challenging problems in bioinformatics and theoretical chemistry. This paper models PSP as a multi-objective optimization problem and adopts ADEMO/D (Adaptive Differential Evolution for Multi-objective Problems based on Decomposition) on its optimizer platform. ADEMO/D has been previously applied to multi-objective optimization with a lot of success. It incorporates concepts of problem decomposition and mechanisms of mutation strategies adaptation. Decomposition-based multi-objective optimization tends to be more efficient than other techniques in complex problems. Adaptation is particularly important in bioinformatics because it can release practitioners, with a great expertise focused on the application, from tuning optimization algorithm’s parameters. ADEMO/D for PSP needs a decision maker and this work tests four different methods. Experiments consider off-lattice models and ab initio approaches for six real proteins. Results point ADEMO/D as a competitive approach for total energy and conformation similarity metrics. This work contributes to different areas ranging from evolutionary multi-objective optimization to bioinformatics as it extends the application universe of adaptive problem decomposition-based algorithms, which despite the success in various areas are practically unexplored in the PSP context.     

From bar diagrams to letter-symbolic algebra: a technology-enabled bridging

In the Singapore primary school Mathematics curriculum, students are taught the model method that uses bar diagrams to visualize the problem structure in a given word problem. When these students progress to secondary school, they learn the algebraic way of solving word problems. Studies (e.g. Ng et al. ) have shown that poor bridging of students from the use of bar diagrams to the use of letter-symbolic algebraic methods can hinder their learning of algebra. We design a software tool 'A lge BAR' to scaffold the learning of the algebraic process, especially the formulation of equations to support a pedagogy that seeks to help students transit from bar diagrams to algebraic methods. In this paper, we report a cycle of evaluation of the intervention pedagogy by examining a class that uses the software tool as part of a holistic intervention package. This evaluation is in the context of an overall design research approach that sought to address complex problems in real classroom contexts in collaboration with practitioners and to integrate design principles with technological affordances to render plausible solutions. Our findings show that the software tool can be an important enabler of the bridging process.     

Construction of a Low Cost Custom 2-D Bar Code Reader for Tube Racks

We describe the development of a custom system for reading 2-D DataMatrix bar codes imprinted on the base of tubes stored in a rack. The system software drives a standard flatbed scanner in order to acquire an image, interprets DataMatrix bar codes at predefined locations in the image, and returns in an array the interpreted bar code data along with other associated information. Commercial software components were used for the image acquisition and bar code interpretation portion of the software. An example is given that illustrates how to call the custom software from Microsoft® Excel and insert the returned data directly into a spreadsheet.     

Optimization of gridshell bar orientation using a simplified genetic approach

Gridshells are defined as structures that have the shape and rigidity of a double curvature shell but consist of a grid instead of a continuous surface. This study concerns those obtained by elastic deformation of an initially flat two-way grid. This paper presents a novel approach to generate gridshells on an imposed shape under imposed boundary conditions. A numerical tool based on a geometrical method, the compass method, is developed. It is coupled with genetic algorithms to optimize the orientation of gridshell bars in order to minimize the stresses and therefore to avoid bar breakage during the construction phase. Examples of application are shown.     

Finite topology variations in optimal design of structures

The method of optimal design of structures by finite topology modification is presented in the paper. This approach is similar to growth models of biological structures, but in the present case, topology modification is described by the finite variation of a topological parameter. The conditions for introducing topology modification and the method for determining finite values of topological parameters characterizing the modified structure are specified. The present approach is applied to the optimal design of truss, beam, and frame structures. For trusses, the heuristic algorithm of bar exchange is proposed for minimizing the global compliance subject to a material volume constraint and it is extended to volume minimization with stress and buckling constraints. The optimal design problem for beam and frame structures with elastic or rigid supports, aimed at minimizing the structure cost for a specified global compliance, is also considered.