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4th International Conference on Contemporary Computing

作者： Agrawal, Rohan Jaypee Inst Informat Technol Dept Comp Sci Noida 201307 Uttar Pradesh India

ISBN: (纸本)9783642226052

A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-objective genetic algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.

关键词： Community Structure Community detection genetic algorithm multi-objective genetic algorithm multi-objective optimization modularity Normalized Mutual Information Bi-objective genetic algorithm

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Variable Neighborhood multiobjective genetic algorithm for the Optimization of Routes on IP Networks

Variable Neighborhood Multiobjective Genetic Algorithm for t...

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6th International Conference on Evolutionary multi-Criterion Optimization

作者： Onety, Renata E. Moreira, Gladston J. P. Neto, Oriane M. Takahashi, Ricardo H. C. Univ Fed Minas Gerais Sch Elect Engn BR-31270901 Belo Horizonte MG Brazil Univ Fed dos Vales Jequitinhonha & Mucuri Otoni MG Brazil Univ Fed Minas Gerais Dept Math Belo Horizonte MG Brazil

ISBN: (纸本)9783642198922

This paper proposes an algorithm to optimize multiple indices of Quality of Service of multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood multiobjective genetic algorithm (VN-MGA), is a genetic algorithm based on the NSGA-II, with the particular feature that different parts of a solution are encoded differently, at Level 1 and Level 2. In order to improve the results, both representations are needed. At Level 1, the first part of the solution is encoded, by considering as decision variables, the arrows that form the routes to be followed by each request (whilst the second part of the solution is kept constant), whereas at Level 2, the second part of the solution is encoded, by considering as decision variables, the sequence of requests, and first part is kept constant. The preliminary results shown here indicate that the proposed approach is promising, since the Pareto-fronts obtained by VN-MGA dominate the fronts obtained by fixed-neighborhood encoding schemes. Besides the potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms.

关键词： Routing on IP Networks Variable Neighborhood Search multi-objective genetic algorithm

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multi-objective optimization of a road diet network design

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2011年第6期45卷 499-511页

作者： Sohn, Keemin Chung Ang Univ Dept Urban Engn Seoul 156756 South Korea

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Road diet Network design multi-objective optimization multi-objective genetic algorithm

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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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