A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design

Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.     

A structured methodology for comparative evaluation of user interface designs using usability criteria and measures

Previous research in software ergonomics has indicated the importance of evaluating the usability of computer user interfaces. This paper presents a quantitative basis for selecting from among multiple alternative interfaces relative to multiple criteria of usability concern. The proposed model consists of two main phases: the prescreening phase and the evaluation phase. The prescreening phase involves expert judgment-based assessment with qualitative criteria. The first phase uses absolute measurement analytic hierarchy process (AHP) to filter possible alternative interfaces to a reasonable subset. The evaluation phase involves user-based assessment such as user testing, with quantitative criteria. The objective of the second phase is to evaluate a subset of alternatives using objective measures and to select the best alternative. A set of criteria and measures for evaluating the usability of computer software designs is presented. The proposed methodology is demonstrated in the interface design of a database system used to analyze trip cases information of nuclear power plant.Relevance to industry

There is currently a focus on the usability of interactive computer software. Software developers, interface designers or human factors engineers often confront the task of comparative evaluation among systems, versions or interface designs. The proposed methodology provides practitioners with a structured approach to select the best interface based on usability criteria and measures.     

User-system cooperative evolutionary computation for both quantitative and qualitative objective optimization in image processing filter design

This study proposes a cooperative evolutionary optimization method between a user and system (CEUS) for problems involving quantitative and qualitative optimization criteria. In a general interactive evolutionary computation (IEC) model, both the system and user have their own role in the evolution, such as individual reproduction or evaluation. In contrast, the proposed CEUS allows the user to dynamically change the allocation of search roles between the system and user, resulting in simultaneous optimization of qualitative and quantitative objective functions without increasing user fatigue. This is achieved by a combination of user evaluation prediction and the integration of interactive and non-interactive EC. For instance, the system performs a global search at the beginning, the user then intensifies the search area, and finally the system conducts a local search in the intensified search area. This study applies CEUS to an image processing filter design problem that involves both quantitative (filter output accuracy) and qualitative (filter behavior) criteria. Experiments have shown that the proposed CEUS can design image filters in accordance with user preferences, and CEUS interacting with a non-naive user enhanced the initial global search so that it converged and found a reasonable solution more than four times faster than a non-interactive search.     

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.     

云模型的布谷鸟搜索算法*

针对布谷鸟搜索算法(CS)存在的不足,优化布谷鸟搜索算法求解连续函数问题的性能,结合云模型在定性与定量之间相互转换的优良特性,设计出云模型的布谷鸟搜索算法(CCS)。其核心思想是通过云模型实现布谷鸟的进化学习过程,类似差分进化进行群体间的信息交流。经过10个测试函数的实验仿真,测试结果表明该文算法能有效改善求解连续函数优化问题的性能。同时,针对连续函数优化问题,该算法与其它算法相比是有更好性能的优化算法。     

Facility layout design using a multi‐objective interactive genetic algorithm to support the DM

The unequal area facility layout problem (UA‐FLP) has been addressed by many methods. Most of them only take aspects that can be quantified into account. This contribution presents a novel approach, which considers both quantitative aspects and subjective features. To this end, a multi‐objective interactive genetic algorithm is proposed with the aim of allowing interaction between the algorithm and the human expert designer, normally called the decision maker (DM) in the field of UA‐FLP. The contribution of the DM's knowledge into the approach guides the complex search process, adjusting it to the DM's preferences. The entire population associated to facility layout designs is evaluated by quantitative criteria in combination with an assessment prepared by the DM, who gives a subjective evaluation for a set of representative individuals of the population in each iteration. In order to choose these individuals, a soft computing clustering method is used. Two interesting real‐world data sets are analysed to empirically probe the robustness of these models. The first UA‐FLP case study describes an ovine slaughterhouse plant and the second, a design for recycling carton plant. Relevant results are obtained, and interesting conclusions are drawn from the application of this novel intelligent framework.     

A communication technology for internet-based collaboration optimization for distributed structural systems

Along with development of incorporation of world economy, collaborative design and collaborative manufacture of enterprises often occur. Subsystems of a complex product system are often analyzed and optimized individually by different design units distributed at different places. In order to find the optimum designs of overall product systems automatically, the internet-based global coordinating optimization is necessary. In this paper, an email-based communication technology of global coordinative optimization of distributed structural systems is proposed, and its realizing approach, communication, process control, practical software and application example are presented.     

A quantitative multimodal discourse analysis of teaching and learning in a web-conferencing environment – The efficacy of student-centred learning designs

This paper presents a quantitative approach to multimodal discourse analysis for analyzing online collaborative learning. The coding framework draws together the fields of systemic functional linguistics and Activity Theory to analyze interactions between collaborative-, content- and technology-related discourse. The approach is used to examine how the task subject matter, the activity design, and the choice of interface affected interaction and collaboration for a computing course conducted in a web-conferencing environment. The analysis revealed the critical impact of activity design on the amount and type of discourse that transpired. Student-centred designs resulted in over six times more student discourse as compared to teacher-centred designs and created a learning environment where students took greater ownership over the tasks and contributed more to the content-based discussion. The paper also incorporates a rationale for the approach to coding and a reflection on its efficacy for discourse analysis in technology-based learning environments.     

基于综合集成法的复杂产品协同设计过程模型及框架研究

建立过程模型及框架,将设计过程有效地表达出来,是协同设计的基础。 论文针对复杂产品设计过程的特征,根据综合集成法,建立了一个以时间、粒度、并行度三 维分布的多层次复杂产品协同设计过程模型。该过程模型在时间维强调多阶段综合,在并行 度维强调多专业综合,在粒度维强调分解与综合的迭代。在该过程模型基础上,结合专家体 系、知识体系、机器体系,提出基于综合集成法的复杂产品协同设计过程框架。该框架中, 机器体系依靠知识体系中的知识,给专家群体提供帮助,专家群体在机器体系提供的项目管 理、群体研讨、协同决策、三维可视化等支持工具帮助下,实现人机结合、多专业结合、多 阶段结合的协同设计过程,以提高复杂产品的协同设计效率。最后介绍了相关原型系统。     

Optimum design of composite laminates for minimum thickness

In this study, an optimization procedure is proposed to minimize thickness (or weight) of laminated composite plates subject to in-plane loading. Fiber orientation angles and layer thickness are chosen as design variables. Direct search simulated annealing (DSA), which is a reliable global search algorithm, is used to search the optimal design. Static failure criteria are used to determine whether load bearing capacity is exceeded for a configuration generated during the optimization process. In order to avoid spurious optimal designs, both the Tsai–Wu and the maximum stress criteria are employed to check static failure. Numerical results are obtained and presented for different loading cases.     

基于强化学习的安全协议形式化验证优化研究

使用形式化方法能够找到安全协议设计中存在的漏洞,但高效地对安全协议进行自动的形式化分析仍然是一个挑战。针对现有形式化自动验证工具无泛化性和效率低的不足,对基于强化学习的安全协议形式化验证框架smartVerif进行优化。使用无人工特征、完全进行自我学习的蒙特卡洛树搜索与深度神经网络相结合的强化学习框架,同时设计能够保留形式化数据结构信息的数据转换方法。实验结果表明,利用该优化方案训练的强化学习模型具有泛化性且能高效地验证安全协议。     

The implementation of a knowledge-based framework for the aerodynamic optimization of a morphing wing device

In the field of aerospace engineering currently a lot of research effort is directed towards the reduction of cruise drag of civil transport aircraft in order to reduce fuel burn, and hence environmental impact and costs. In order to reduce cruise drag, a promising method is under consideration by adjusting, or rather morphing the rear part of the aircraft’s wing during cruise flight. Given the premature state of knowledge of such a design implementation, a knowledge-based computational framework is developed. The purpose of this framework is to allow for an aerodynamic optimization of a section of the wing. The framework is set up in such a way that all relevant design knowledge generated in the process can be captured and used in a subsequent mechanical design process. In this fashion, the complex design process of a novel morphing wing device can be automated to a certain degree. This automation can be used to construct a large number of different feasible and optimized designs with varying boundary conditions of a complex experimental device.This article describes the initial 2-dimensional aerodynamic design step of the morphing device under consideration and how it is implemented in a knowledge-based optimization framework. It describes the initial stage of the development of this tool, as it will be expanded by a number of design steps that each adds more detail to the design in all relevant aspect fields (aerodynamic, structural, actuation, etc.). Ultimately, this tool will be used to obtain a thorough evaluation of a number of different proposed structural solutions and allow for a comparison between them.     

The multi-user computer-aided design collaborative learning framework

New developments to computer-aided design (CAD) software transform a once solitary modelling task into a collaborative one. The emerging multi-user CAD (MUCAD) systems allow virtual, real-time collaboration, with the potential to expand the learning outcomes and teaching methods of CAD. This paper proposes a MUCAD collaborative learning framework (MUCAD-CLF) to interpret backend analytic data from commercially available MUCAD software. The framework builds on several existing metrics from the literature and introduces newly developed methods to classify CAD actions collected from users’ analytic data. The framework contains two different classification approaches of user actions, categorizing actions by action type (e.g., creating, revising, viewing) and by design space (e.g., constructive, organizing), for comparative analysis. Next, the analytical framework is applied via a collaborative design challenge, corresponding to over 20,000 actions collected from 31 participants. Illustrative analyses utilizing the MUCAD-CLF are presented to demonstrate the resulting insight. Differences in CAD behaviour, indicating differences in learning, are observed between teams made up entirely of novices, entirely of experienced users, or a mix. In pairs of experts and novices, we see both a perceived high-satisfaction apprenticeship experience for the novices and preliminary evidence of an increase in expert design behaviours for the novices. The proposed framework is critical for MUCAD systems to make the most of the educational possibility of combining technical skill-building with team collaboration. Preliminary evidence collected in a fully-virtual design learning activity, and analyzed using the proposed MUCAD-CLF, shows that novice students gain advanced CAD design knowledge when collaborating with experienced teammates. With the user data captured by modern MUCAD software and the MUCAD-CLF presented herein, instructors and researchers can more efficiently assess and visualize students’ performance over the design learning process.     

Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers

A new method for optimizing complex engineering designs is presented that is based on the Learnable Evolution Model (LEM), a recently developed form of non‐Darwinian evolutionary computation. Unlike conventional Darwinian‐type methods that execute an unguided evolutionary process, the proposed method, called LEMd, guides the evolutionary design process using a combination of two methods, one involving computational intelligence and the other involving encoded expert knowledge. Specifically, LEMd integrates two modes of operation, Learning Mode and Probing Mode. Learning Mode applies a machine learning program to create new designs through hypothesis generation and instantiation, whereas Probing Mode creates them by applying expert‐suggested design modification operators tailored to the specific design problem. The LEMd method has been used to implement two initial systems, ISHED1 and ISCOD1, specialized for the optimization of evaporators and condensers in cooling systems, respectively. The designs produced by these systems matched or exceeded in performance the best designs developed by human experts. These promising results and the generality of the presented method suggest that LEMd offers a powerful new tool for optimizing complex engineering systems. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1217–1248, 2006.     

Teaching, learning and inquiry strategies using computer technology

This paper reports a case study in a Singapore school where a group of 'at risk' secondary students were given a series of linked computer-based business and consumer projects involving word processing, survey design and analysis, and presentation of results with computer graphics. There were clear benefits in terms of the group's motivation, behaviour and performance on tests, particularly in English. Both quantitative and qualitative data to support the findings are provided. The theoretical underpinnings of constructivism, collaborative inquiry and situated learning provide the conceptual framework for this case study.     

Optimal design methods for a digital human–computer interface based on human reliability in a nuclear power plant part 1: Optimization method for monitoring unit layout

This is the first in a series of papers describing the optimal design method for a digital human-computer interface of a nuclear power plant (NPP) from three different points based on human reliability. The purpose of this series is to propose different optimization methods from varying perspectives to decrease human factor events that arise from the defects of a human–computer interface. The present paper mainly discusses the optimization method for the layout of monitoring units.The layout of relative positions among different functional blocks in a digital human–computer interface influences the time required to search information. The risk of an event increases with increases in the time required to search for information because of the limited time available during a nuclear emergency. To avoid the risk of such an event, the authors propose an optimization method for the layout of monitoring units based on human reliability for a digital human–computer interface of a NPP. In the optimal design process, the authors propose a linear reversal genetic hybridization method that uses the Bayesian method as an adaptive function and takes human reliability as the optimized criterion. To quantitatively obtain the probability of human reliability, the authors use dynamic simulative functions including time and human factors. Finally, an experiment is conducted. The results indicate that the linear reversal genetic hybridization method has good stability and sensitivity and that the proposed optimization method has good accuracy and convergence.     

Application of design parameters space search for belt conveyor design

Belt conveyor design is examined as an application of a proposed Design Parameters Space Search technique. First, the main characteristics of the belt-conveyor design process are presented as they appear in the current literature. Furthermore, a proposed general knowledge-representation platform is described, and its ability to house the relevant conveyor design knowledge is also shown.

Next, the extended search technique of the design space is discussed, and an integrated example of a belt-conveyor design is presented, based on the proposed representation platform and the extended search technique. Finally, it is shown that the design of belt conveyors according to the proposed approach presents the following significant advantages:

• • Due to the knowledge-representation scheme adopted, both qualitative and quantitative knowledge can be used within the same platform.

• • Multiple solutions can be easily produced through user-defined design criteria. These solutions can be further modified and/or evaluated to produce more-specific designs.

• • The required for user-input data is kept to a minimum. Due to the applied extended search method, semiautomatic design can be achieved. As a consequence, the design process is completed in less time than that required by the conventional methods.

     

两级多品种生产调度的专家系统

本文以一类两级多品种加工过程的生产调度为背景,提出了一种将人工智能的启发式搜 索优化技术与专家经验规则相结合的专家系统.研究了系统的知识表达、组成与结构,该系统 能方便地处理带有定性约束的问题,并利用问题的知识背景显著地提高了系统的求解效率.最 后给出了在大型炼油厂润滑油系统生产调度中应用的实例.