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

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第4期60卷 760-768页

作者： Yang, Chih-Chieh So Taiwan Univ Dept Multimedia & Entertainment Sci Yung Kang 71005 Tainan County Taiwan

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 (EKES) 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. EKES 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. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Product form design Kansei engineering Support vector regression multi-objective genetic algorithm

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Sequencing Mixed-model Production Systems by Modified multi-objective genetic algorithms

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Chinese Journal of Mechanical Engineering 2010年第5期23卷 537-546页

作者： WANG Binggang Department of Industry and Business Administration Henan University of Urban Construction Pingdingshan 467044 China

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many ***,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly *** far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly *** paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate *** objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly *** integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are *** the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ *** feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing *** sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.

关键词： mixed-model production system sequencing parallel machine buffers multi-objective genetic algorithm multi-objective simulated annealing algorithm

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Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2011年第6期34卷 1818-1826页

作者： Liu, Wenjia Chen, Bo Michigan Technol Univ Dept Mech Engn Engn Mech Dept Elect & Comp Engn Houghton MI 49931 USA

This paper studies optimal control of mobile monitoring agents in artificial-immune-system-based (AIS-based) monitoring networks. In AIS-based structural health monitoring (SHM) networks, the active structural health monitoring is performed by a group of mobile monitoring agents equipped with damage pattern recognition algorithms. The mobile monitoring agents mimic immune cells in the natural immune system and patrol a structure to detect damage patterns using their receptors (feature vectors), damage pattern recognition algorithms, and the dynamic response data of the structure. The optimal control of mobile monitoring agents includes agent generation and distribution. The generation of mobile monitoring agents is optimized to minimize the response time for the mobile monitoring agents to diagnose structural damage in a sub-network and maximize the average affinity of monitoring agents' receptors to the damaged sensor data feature vector. The objective functions for distributing mobile monitoring agents are to increase the detection probability and extend network life by balancing energy consumption of sensor nodes in the network. The presented optimization algorithms are developed using multi-objective genetic algorithms. The impact of the algorithm parameters on the performance of the algorithm is also investigated. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Mobile monitoring agent Artificial immune system

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An Improved genetic algorithm for multi-objective Flexible Job-shop Scheduling Problem

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International Conference on Manufacturing Science and Engineering (ICMSE 2009)

作者： Zhang, Chaoyong Wang, Xiaojuan Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Dept Ind & Mfg Syst Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9780878492800

Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.

关键词： Flexible job shop scheduling multi-objective genetic algorithm Pareto-optimality immune and entropy principle

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multi-point and multi-objective optimization design method for industrial axial compressor cascades

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2011年第C6期225卷 1481-1493页

作者： Ju, Y. P. Zhang, C. H. Xi An Jiao Tong Univ Dept Fluid Machinery & Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China

Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA-BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5 degrees, -9 degrees, and 13 degrees, respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

关键词： axial compressor cascade optimization design multi-point multi-objective genetic algorithm design of experiment artificial neural network Pareto optimum

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Springback control of sheet metal forming based on FEA and multi-objective genetic algorithm

Springback control of sheet metal forming based on FEA and m...

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第五届国际材料与热加工物理模拟及数值模拟学术会议

作者： Liu Wei* Yang Yuying Xing Zhongwen P. O. Box 435 School of Materials Science and Engineering Harbin Institute of Technology Harbin China 150001

In this paper, an automated optimization method by the integration of finite element analysis and optimization algorithm was presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, multi-objective genetic algorithm was applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. Furthermore, response surface model was employed to be a fast analysis tool to surrogate the time-consuming FEA procedure in the iterations of multi-objective genetic algorithm. An example was studied to illustrate the application of the approach proposed, and the authors came to the conclusion that the proposed method is more efficient than traditional manual FEA procedure and the ’trial and error’ approach for springback controlling.

关键词： springback sheet metal FEA multi-objective genetic algorithm

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A Study on Analysis of Design Variables in Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine

A Study on Analysis of Design Variables in Pareto Solutions ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Kudo, Fumiya Yoshikawa, Tomohiro Furuhashi, Takeshi Nagoya Univ Grad Sch Engn Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781424478354

genetic algorithm (GA)[1] is one of the most effective methods in the application to optimization problems. Recently, multi-objective genetic algorithm (MOGA) is focused on in the engineering design field. In this field, the analysis of design variables in the acquired Pareto solutions, which gives the designers useful knowledge in the applied problem, is important as well as the acquisition of advanced solutions. This paper proposes a new visualization method using Isomap which visualizes the geometric distances of solutions in the design variable space considering their distances in the objective space. The proposed method enables a user to analyze the design variables of the acquired solutions considering their relationship in the objective space. This paper applies the proposed method to the conceptual design optimization problem of hybrid rocket engine and studies the effectiveness of the proposed method. It shows that the visualized result gives some knowledges on the features between design variables and fitness values in the acquired Pareto solutions.

关键词： Analysis of Design Variables Visualization Isomap multi-objective genetic algorithm Conceptual Design Optimization Problem of Hybrid Rocket Engine

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Sorting Unsigned Permutations by Reversals using multi-objective Evolutionary algorithms with Variable Size Individuals

Sorting Unsigned Permutations by Reversals using Multi-Objec...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Ghaffarizadeh, Ahmadreza Ahmadi, Kamilia Flann, Nicholas S. Utah State Univ Dept Comp Sci Logan UT 84322 USA

ISBN: (纸本)9781424478354

Sorting by reversals is a simplified version of the genome rearrangement problem that seeks to discover the evolutionary relationship between different genomes, and is one of the many challenging problems in Bioinformatics. Solving the problem optimally has been proved to be NP-Hard and so a selection of approximation algorithms have been developed. In this paper a new mapping order is introduced to solve the problem of sorting unsigned permutations using a specialized multi-objective genetic algorithm. Our modified genetic algorithm uses a population with variable length individuals to maintain a worst time running time complexity of O(n(4) log(2) n), where n is the problem size. The results show that this approach is more effective than the 3/2 heuristic method and previous genetic algorithm approaches.

关键词： Sorting by reversals genome rearrangement sorting unsigned permutations variable size individuals multi-objective genetic algorithm

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Evolutionary multi-objective Design Optimisation of Energy Harvesting MEMS The Case of a Piezoelectric

Evolutionary Multi-objective Design Optimisation of Energy H...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Benkhelifa, Elhadj Moniri, Mansour Tiwari, Ashutosh de Rueda, Alfonso G. Staffordshire Univ Fac Comp Engn & Technol Stafford England Cranfield Univ Decis Engn Cent Mfg Dept Cranfield MK43 0AL Beds England

ISBN: (纸本)9781424478354

The design and optimisation of Energy Harvesting (EH) Micro-Electromechanical-Systems (MEMS) is of particular interest in this research. The application of such devices is becoming an attractive alternative to the traditional use of batteries in wireless and body sensor networks. An evolutionary multi-objective Design Optimisation (DO) Framework is developed to experiment with one class of EH-MEMS, namely, Piezoelectric, using a reconstructed analytical model of the system. The application of such a Framework in this application domain is unprecedented and has already shown very promising results and in some cases it outperformed the human engineer. A thorough analysis of the results has been undertaken, which reveals interesting conclusions about the behaviour and physics of such devices. Besides, the main features of the Framework are explored enabling the enhancement of the MEMS-DO.

关键词： multi-objective genetic algorithm design optimisation Energy Harvesting MEMS evolutionary computing Piezoelectric mobility

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Classifier Ensembles Optimization Guided by Population Oracle

Classifier Ensembles Optimization Guided by Population Oracl...

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IEEE Congress on Evolutionary Computation (CEC)

作者： dos Santos, Eulanda M. Sabourin, Robert Univ Fed Amazonas Manaus Amazonas Brazil Ecole Technol Super Montreal PQ Canada

ISBN: (纸本)9781424478354

Dynamic classifier ensemble selection is focused on selecting the most confident classifier ensemble to predict the class of a particular test pattern. The overproduce-and-choose strategy is a dynamic classifier ensemble selection method which is divided into optimization and dynamic selection phases. The first phase involves the test of different candidate ensembles in order to produce a population composed of the highest performing candidate ensembles. Then, the second phase calculates the domain of expertise of each candidate ensemble to pick up the solution with highest degree of certainty of its decision to classify the unknown test samples. It has been shown that the optimization phase decreases oracle, the upper bound of dynamic selection processes. In this paper we propose a hybrid algorithm to perform the optimization phase of overproduce-and-choose strategy. The proposed algorithm combines stochastic initialization of candidate ensembles of different sizes, with the traditional forward search greedy method. The objective is to apply oracle as search criterion during the optimization phase. We show experimentally that choosing the population of classifier ensembles taking into account the population oracle leads to increase the upper bound of the dynamic selection phase. Moreover, experimental results conducted to compare the proposed method to a multi-objective genetic algorithm (MOGA), demonstrate that our method outperforms MOGA on generating population of candidate ensembles with higher oracle rates.

关键词： Dynamic classifier ensemble selection hybrid search algorithm multi-objective genetic algorithm pattern recognition

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