Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

A support vector regression based prediction model of affective responses for product form design

In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers’ affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR–RCGA) is compared to that of SVR with 5-fold cross-validation (SVR–5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN–5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.     

Kansei engineering as a powerful consumer-oriented technology for product development

Kansei engineering was founded 30 years ago, as an ergonomics and consumer-oriented technology for producing a new product. When a consumer wants to buy something, he/she will have a kind of feeling and image (kansei in Japanese) in his/her mind. If the consumer's feeling could be implemented in the new product, he/she would be more satisfied with the product. Kansei engineering aims at translation of kansei into the product design field including product mechanical function. This is why it is called the consumer-oriented aspect. There are many products in Japan which have applied kansei engineering. Recently, it has also been applied to construction products as well as to community design.     

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.     

Memory-based adaptive partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms

A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is presented in this work. This algorithm works by performing an adaptive-probabilistic refinement of the search space, with no aggregation in objective space. This work investigated the integration of MAP within the state-of-the-art fast and elitist non-dominated sorting genetic algorithm (NSGAII). Considerable improvements in convergence were achieved, in terms of both speed and accuracy. Results are provided for several commonly used constrained and unconstrained benchmark problems, and comparisons are made with standalone NSGAII and hybrid NSGAII-efficient local search (eLS).     

A Kansei mining system for affective design

Affective design has received much attention from both academia and industries. It aims at incorporating customers' affective needs into design elements that deliver customers' affective satisfaction. The main challenge for affective design originates from difficulties in mapping customers' subjective impressions, namely Kansei, to perceptual design elements. This paper intends to develop an explicit decision support to improve the Kansei mapping process by reusing knowledge from past sales records and product specifications. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with the mapping of affective needs. A Kansei mining system is developed to utilize valuable affect information latent in customers' impressions of existing affective designs. The goodness of association rules is evaluated according to their achievements of customers' expectations. Conjoint analysis is applied to measure the expected and achieved utilities of a Kansei mapping relationship. Based on goodness evaluation, mapping rules are further refined to empower the system with useful inference patterns. The system architecture and implementation issues are discussed in detail. An application of Kansei mining to mobile phone affective design is presented.     

提高多目标进化算法分布性的动态调整机制

为提高多目标进化算法的分布性,提出一种基于极坐标的动态调整机制。在极坐标下,根据解集的拥挤程度,计算个体解的缩放系数。在进化过程中利用该缩放系数动态调整解集支配关系,适当提高分布性好的解在支配关系中的地位以改善解的分布。对测试函数的仿真试验结果表明,将该机制应用于经典算法能显著提高算法的分布性,同时保持良好的收敛性。     

基于犹豫模糊语言术语集和粒子群优化算法的产品造型设计感性评价方法

产品造型感性评价反映了用户的意象感知,具有模糊性与不确定性,用户常难以准确描述其感性偏好而表现出犹豫.针对该问题,引入犹豫模糊语言术语集(HFLTSs)描述用户感性评价,基于其数学算子构建犹豫模糊语言共识模型以测度用户认知一致性程度,借助粒子群优化算法(PSO)实现非共识条件下用户评价矩阵的优化与共识达成,通过逼近理想...     

Three self-adaptive multi-objective evolutionary algorithms for a triple-objective project scheduling problem

Finding a Pareto-optimal frontier is widely favorable among researchers to model existing conflict objectives in an optimization problem. Project scheduling is a well-known problem in which investigating a combination of goals eventuate in a more real situation. Although there are many different types of objectives based on the situation on hand, three basic objectives are the most common in the literature of the project scheduling problem. These objectives are: (i) the minimization of the makespan, (ii) the minimization of the total cost associated with the resources, and (iii) the minimization of the variability in resources usage. In this paper, three genetic-based algorithms are proposed for approximating the Pareto-optimal frontier in project scheduling problem where the above three objectives are simultaneously considered. For the above problem, three self-adaptive genetic algorithms, namely (i) A two-stage multi-population genetic algorithm (MPGA), (ii) a two-phase subpopulation genetic algorithm (TPSPGA), and (iii) a non-dominated ranked genetic algorithm (NRGA) are developed. The algorithms are tested using a set of instances built from benchmark instances existing in the literature. The performances of the algorithms are evaluated using five performance metrics proposed in the literature. Finally according to the technique for order preference by similarity to ideal solution (TOPSIS) the self-adaptive NRGA gained the highest preference rank, followed by the self-adaptive TPSPGA and MPGA, respectively.     

Relationship between eye fixation patterns and Kansei evaluation of 3D chair forms

Understanding how to induce Kansei (emotion or affect) in consumers through form is critical in product design and development. Conventional Kansei evaluations, which involve subjectively evaluating the overall form of a product, do not clarify the effects of the individual parts of a product on people’s Kansei evaluation. A microscale analysis of eye movement of people looking at product form may redeem this flaw in subjective evaluation. However, simultaneously recording eye movement when people making Kansei evaluation is challenging, previous studies have typically investigated either the relationship between form and eye movement or the relationship between form and Kansei separately. The eye movement of people while performing Kansei evaluations on product forms still has not been clarified. To address this issue, the present study used an eye tracking system to analyze the changes in the fixation points of people performing various Kansei evaluations. Twenty participants were recruited for 8 Kansei evaluations on the form of 16 chairs by using the semantic differential (SD) rating, while their eye movements on these evaluations were tracked simultaneously. Through factor analysis on the data of Kansei evaluations, two principal factors, valence (pleasure) and arousal, were extracted from the 8 Kansei scales to constitute a Kansei plane which is compatible to Russell’s circumplex model (plane) of affect By adopting the factor scores of the 16 chairs as coordinates, the 16 chairs were mapped into the Kansei plane. Further analysis on the eye fixation on the chairs located in this plane concluded the following results: (a) Pleasure had a more significant effect on the participants’ visual attention compared to arousal; the participants required more fixation points when evaluating the chair form that induced displeasure. (b) The participants typically fixated on two parts of the chairs during their Kansei evaluations, namely the seat and the backrest, indicating that seats and backrests are the two primary features people consider when evaluating chairs. The results clarify the effect of various Kansei on eye movements; thereby enable predicting people’s Kansei evaluations of product forms through analyzing their eye movement.     

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several subpopulations or islands, each of them being in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. Evaluation of complete solutions is achieved through cooperation, i.e., all subpopulations share a subset of their current partial solutions. Our purpose is to study how the performance of the cooperative coevolutionary multi-objective approaches can be drastically increased with respect to their corresponding original versions. This is specially interesting for solving complex problems involving a large number of variables, since the problem decomposition performed by the model at the island level allows for much faster executions (the number of variables to handle in every island is divided by the number of islands). We conduct a study on a real-world problem related to grid computing, the bi-objective robust scheduling problem of independent tasks. The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks). We propose a parallel, multithreaded implementation of the coevolutionary algorithms and we have analyzed the results obtained in terms of both the quality of the Pareto front approximations yielded by the techniques as well as the resulting speedups when running them on a multicore machine.     

Integrated product line design and supplier selection: A multi-objective optimization paradigm

Product line design is commonly used to provide higher product variety for satisfying diversified customer needs. To reduce the cost and development time and improve quality of products, companies quite often consider sourcing. Conventionally, product line design and supplier selection are dealt with separately. Some previous studies have been attempted to consider product line design and supplier selection simultaneously but two shortcomings were noted. First, the previous studies considered several objectives as a single objective function in the formulation of optimization models for the integrated problem. Second, positions of product variants to be offered in a product line in competitive markets are not clearly defined that would affect the formulation of marketing strategies for the product line. In this paper, a methodology for integrated product line design and supplier selection is proposed to address the shortcomings in which a multi-objective optimization model is formulated to determine their specifications and select suppliers for maximizing the profit, quality and performance as well as minimizing the cost of the product line. In addition, joint-spacing mapping is introduced to help estimate market share of products and indicate positions of product variants. The proposed methodology can provide decision makers with a better tradeoff among various objectives of product line design, and define market positions of product variants explicitly. The results generated based on the methodology could help companies develop product lines with higher profits, better product quality and larger market share to be obtained. A case study of a product line design of notebook computers was performed to illustrate the effectiveness of the proposed methodology. The results have shown that Pareto optimal product line designs and the specifications of product variants can be determined. Suppliers of components and modules can be selected with considerations of minimum sourcing cost, and maximum performance and quality of product variants. Prices and positions of the product variants can also be determined.     

Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms

Characterization of dynamism is an essential phase for some of the dynamic multi-objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although frequency of change and severity of change are the two main perspectives of characterizing dynamic features of the dynamic multi-objective optimization problems (DMOPs), they do not sufficiently attract attentions of the research community. In this paper, we propose a set of new sensor-based change detection schemes for the DMOPs that significantly outperform the current used change detection schemes. Additionally, a new technique is proposed for detecting the change severity for DMOPs. The experimental evaluation based on different test problems and change severity levels validates performance of our technique. We also propose a novel adaptive algorithm called change-responsive NSGA-II (CR-NSGA-II) algorithm that incorporates the change detection schemes, the technique for change severity and a new response mechanism into the NSGA-II algorithm. Our algorithm demonstrates competitive and significantly better results than the leading DMOEAs on majority of test problems and metrics considered.     

约束多目标进化算法研究进展

约束多目标进化算法（CMOEAs）能够同时处理多个相互冲突的目标函数和约束条件,引导种群逼向可行域的最优解,受到了研究者的广泛重视。首先介绍了约束多目标优化问题（CMOPs）的相关定义和多目标进化算法（MOEAs）的三种分类;其次,系统地分析了当前CMOEAs中约束处理机制,凝练出当前主要的四种约束处理方法;然后,从基于支配、基于指标、基于分解三个方面对CMOEAs的研究进展进行了详细综述;最后,指明了CMOEAs存在的挑战和未来研究方向。     

Preference-based multi-objective evolutionary algorithms for power-aware application mapping on NoC platforms

Network-on-chip (NoC) are considered the next generation of communication infrastructure in embedded systems. In the platform-based design methodology, an application is implemented by a set of collaborative intellectual property (IP) blocks. The selection of the most suited set of IPs as well as their physical mapping onto the NoC infrastructure to implement efficiently the application at hand are two hard combinatorial problems that occur during the synthesis process of Noc-based embedded system implementation. In this paper, we propose an innovative preference-based multi-objective evolutionary methodology to perform the assignment and mapping stages. We use one of the well-known and efficient multi-objective evolutionary algorithms NSGA-II and microGA as a kernel. The optimization processes of assignment and mapping are both driven by the minimization of the required silicon area and imposed execution time of the application, considering that the decision maker’s preference is a pre-specified value of the overall power consumption of the implementation.     

A systematic knowledge-based method for design of transformable product

A transformable product can perform different functions or change functionality by changing its physical structure. It is formed by integration of different components whose states can be transformed each other. However, there is a lack of systematic methods to guide design of the transformable product. In order to improve the design efficiency of transformable products, a large number of products are studied in this research to build a case base of transformation parameters and transformation principles for the design knowledge. A systematic design process is proposed to apply the design knowledge. The transformation design problem is first mapped from the problem domain to the knowledge domain expressed by transformation parameters. A general solution is then obtained in the knowledge domain. A multi-classification support vector machine is used to train a model of the transformation recommendation based on transformation parameters. Finally, the general solution is mapped into the problem domain for the specific solution using the analogy design. The effectiveness of the method is verified in the design of a self-propelled boom sprayer.     

Knowledge management of product design: A requirements-oriented knowledge management framework based on Kansei engineering and knowledge map

For the knowledge management of product design, knowledge innovation is the foundation and motivation for the independent innovation and enhancing the core competitiveness. Most of the product knowledge exists in the brain of designers. How to obtain the required knowledge accurately in massive knowledge database becomes the key to knowledge innovation. However, the design knowledge based on consumer’s requirement has not been extensively studied. There is no consensus on the reasonable and effective implementation of the knowledge management framework to select the optimum design knowledge based on the consumer’s requirements. In this study, to efficiently realize the knowledge acquisition and knowledge selection, a requirements-oriented knowledge management model is established, with the advantage of Kansei engineering in knowledge acquisition and multi-objective decision-making in knowledge selection. Finally, the outdoor leisure chairs design is used as a case study to explain the implementation of the knowledge management framework. To reveal the advantages of the framework, it was compared with other frameworks. The results show that the proposed knowledge management framework is more efficient and provided a method of designers to acquire design knowledge based on the consumer’s requirements.     

A methodology for evolutionary product design

this paper describes a function-based approach for conceptual design support in the context of evolutionary product development. The main objective is to improve a designers productivity by the effective reuse of existing design information in design alternative identification, evaluation, and modification. An integrated evolutionary design methodology, EPD, is presented. The proposed methodology divides the whole process into three inter-related phases: information recovery, information management, and information reuse. The detailed steps in each phase are elaborated, and various techniques are employed to improve information reuse efficiency. A case study on commercial electrostatic air cleaner was used to illustrate the whole process of product evolutionary design. The proposed methodology will have a positive impact on the future development of the conceptual design support system.     

A digital twin-enhanced system for engineering product family design and optimization

Engineering product family design and optimization in complex environments has been a major bottleneck in today’s industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.