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International Conference on Future Information Engineering(FIE 2014)

作者： Roya M.Ahari S.T.A.Niaki Dept. of Industrial Engineerin Sharif University of Technology

Contractor selection for a project is an important decision, one for the project time and cost, next for the quality obtained by the project. Although the project managers can easily determine the project time and cost, the quality is usually undefined especially for un-experienced managers. With a learnable property, an approach is first introduced in this paper to quantify the quality obtained for a gas well drilling project. Then, based on these three objectives（time, cost, and quality）, a contractor selection problem is converted to an optimization problem. Next, the NSGA-II algorithm is utilized for solution. At the end, a sensitivity analysis is performed to select the parameters of the algorithm.

关键词： Contractor selection Quality evaluation multi-objective optimization Gas well drilling project multi-objective genetic algorithm

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A multi-drive Data Mining algorithm Based on Classification Method and Its Application in Investment Decision

A Multi-drive Data Mining Algorithm Based on Classification ...

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第七届模糊信息与工程国际会议(ICFIE'2014)、第一届运筹与管理国际会议 (ICORG'2014)暨中国运筹学会模糊信息与工程分会第七届年会、广东省运筹学会第二届年会、粤港澳运筹学会首届年会

作者： Yubin Zhong Li Tao Jialiang Xuan Yifan Zhang College of Mathematics and Information Science Guangzhou University College of Civil Engineering Guangzhou University UQ Business School the University of Queensland

This paper discusses the development of strategies in big data context,proposing a multi-drive data mining algorithm based on classification method and solving the investment decision problem when educational charity Goodgrant is planning to invest in 7900 colleges in the U.S. This paper first classifies all the colleges into to-be funded and to-be eliminated based on cluster analysis. Then, run the Fisher discriminant function parameters of the to-be funded colleges. After determining 52 indices which can influence investment return, select 39 effective indices according to Bayesian discriminant and calculate index weights. In the end,build a multi-objective optimal model based on the maximum of Fisher discriminant function and investment return, apply multi-objective genetic algorithm to compute the optimal investment weights of all the colleges and produce investment priority ranking according to the investment weight. The method in this paper can also be used in other investment area among big data context.

关键词： investment decision cluster analysis Bayesian discriminant Fisher discriminant multi-objective genetic algorithm

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Hybrid genetic-Fuzzy algorithm for Variable Selection in Spectroscopy

Hybrid Genetic-Fuzzy Algorithm for Variable Selection in Spe...

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12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013

作者： de Lima, Telma Woerle Soares, Anderson da Silva Coelho, Clarimar Jose Salvini, Rogerio Lopes Laureano, Gustavo Teodoro Univ Fed Goias Inst Informat Goiania Go Brazil Pontifical University Catholic Goias Dept Comp Sci Goiania Go Brazil

ISBN: (纸本)9783642386091

This paper presents a hybrid multi-objective genetic fuzzy algorithm for the variable-selection problem in spectroscopy. The problem formulation considers three fitness functions related to linear equations system stability. These fitness functions are models with fuzzy sets that evaluate the fitness solution for pick out the best to crossover. The population diversity is obtained applying the crowding distance method. The study shows that the selection by a fuzzy decision has better results than the selection by non-domination in problems where the fitness weighing is more proper than no-domination solutions.

关键词： multi-objective genetic algorithm Fuzzy logic Variable Selection

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Feature extraction and track measurement for low resolution images: A cost effective method

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RADIATION MEASUREMENTS 2013年 50卷 228-231页

作者： Khayat, O. Ghergherehchi, M. Afarideh, H. Durrani, S. A. Pouyan, Ali A. Amirkabir Univ Technol Nucl Engn & Phys Dept Tehran Iran Univ Birmingham Sch Phys & Astron Birmingham W Midlands England Shahrood Univ Technol Dept Comp Engn Shahrood Iran

In a track counting system a digital USB CCD camera is often used to make a communication between the visually seen etched tracks of a solid state nuclear track detector and the image analyzer software installed on the PC. The camera's characteristics determine the size of the image, and the number of pixels per track. The resolution and contrast of the images are the key features in how well an image analyzer can perform the task of track measurement. In this paper we present an approach to increase the resolution and contrast of the images, aiming to extract the features which cannot be simply attained from the original image. The presented algorithm as an image processing technique is used to replace the economical cost of high quality imaging system with the computational cost of image processing. The tracks' characteristics will remain intact and the contrast of the image will be enhanced through Shannon entropy reaching its maximum. Feature preservation is done through maximizing the similarity between the original low resolution image and the processed higher resolution image. Similarity is defined as the mutual information between two images. This resolution increment will result in contrast enhancement and increased information transfer for feature extraction purpose. Experimental images of alpha tracks taken by a 350KP USB digital camera are used to verify the efficiency of the proposed algorithm. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Shannon entropy Mutual information Solid state nuclear track detector Feature preservation

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MOGA-based fuzzy data mining with taxonomy

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KNOWLEDGE-BASED SYSTEMS 2013年第Dec.期54卷 53-65页

作者： Chen, Chun-Hao He, Ji-Syuan Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei 251 Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan

Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since membership functions may have a critical influence on the final mining results, several genetic-fuzzy mining approaches have been proposed for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on a single level and consider only one objective function. This paper proposes a multi-objective multi-level genetic-fuzzy mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. The algorithm first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. Two objective functions are then considered. The first one is the knowledge amount mined out at different levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated using these two objective functions. After the evolutionary process terminates, various sets of membership functions can be used for deriving multi-level fuzzy association rules according to decision-makers. Experimental results on the simulated and real datasets show the effectiveness of the proposed algorithm. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Data mining Fuzzy sets Fuzzy rules multi-objective genetic algorithm Taxonomy

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Economic and environmental impacts of the energy source for the utility production system in the HDA process

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ENERGY CONVERSION AND MANAGEMENT 2013年 74卷 129-139页

作者： Ouattara, A. Pibouleau, L. Azzaro-Pantel, C. Domenech, S. Inst Natl Polytech Houphouet Boigny Dept Genie Chim & Agroalimentaire Yamoussoukro Cote Ivoire ENSIACET INPT CNRS LGCUMR 5503 F-31432 Toulouse 4 France

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This "gate-to-gate" environmental study is then enlarged by implementing a "cradle-to-gate" Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Hydrodealkylation of toluene multi-objective genetic algorithm Energy source Environmental burdens Life Cycle Assessment

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multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings

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JOURNAL OF VIBRATION AND CONTROL 2013年第6期19卷 942-960页

作者： Cha, Young-Jin Kim, Yeesock Raich, Anne M. Agrawal, Anil K. CUNY City Coll New York NY 10031 USA Worcester Polytech Inst Worcester MA 01609 USA Lafayette Coll Easton PA 18042 USA

This paper investigates the multi-objective optimization of active control systems for vibration control of three-dimensional (3D) high-rise buildings under a variety of earthquake excitations. To this end, a novel multi-objective genetic algorithm is developed through the integration of the best features of a non-dominated sorting II (NS2) genetic algorithm (GA) and an implicit redundant representation (IRR) GA. The proposed NS2-IRR GA finds not only minimum distributions of both actuators and sensors within structures, but also minimum dynamic responses of 3D structures. Linear quadratic Gaussian controllers, hydraulic actuators and accelerometers are used for implementation of active control systems within the 3D buildings. To demonstrate the effectiveness of the proposed NS2-IRR GA, two 3D building models are investigated using finite element methods, including low-and high-rise buildings. It is shown that the proposed NS2-IRR GA is effective in finding not only optimal locations and numbers of both actuators and sensors in 3D buildings, but also minimum responses of the 3D buildings. The simulation also shows that the control performances of the proposed approach significantly enhance those of the engineering judgment oriented benchmark layout, which is validated by comparisons of each performance using the same number of actuators.

关键词： Actuators multi-objective genetic algorithm optimal placement sensors

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Optimal capacity design of eccentrically braced steel frameworks using nonlinear response history analysis

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ENGINEERING STRUCTURES 2013年 48卷 28-36页

作者： Gong, Yanglin Xue, Yusong Xu, Lei Lakehead Univ Dept Civil Engn Thunder Bay ON P7B 5E1 Canada Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

Presented in this paper is an optimal capacity design method for steel building frameworks by adopting a nonlinear response history analysis procedure as the evolution tool for the structures under extreme earthquake loading. Minimum structural weight, minimum seismic input energy, and maximum hysteretic energy of fuse members are identified as design objectives. The design constraints include plastic deformation limit on every frame member and total deformation limit on each inter-story drift. The design optimization method employs a multi-objective genetic algorithm to search for optimal member section sizes from among commercially available steel section shapes. The design method is illustrated in details for a three-story eccentrically-braced steel frame. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Structural optimization Seismic design Capacity design Steel frames Eccentrically-braced frames Nonlinear response history analysis multi-objective genetic algorithm

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A two-stage optimal planning and design method for combined cooling, heat and power microgrid system

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ENERGY CONVERSION AND MANAGEMENT 2013年 74卷 433-445页

作者： Guo, Li Liu, Wenjian Cai, Jiejin Hong, Bowen Wang, Chengshan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Sun Yat Sen Univ Sch Phys & Engn Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Sino French Inst Nucl Engn & Technol Guangzhou 510275 Guangdong Peoples R China

In this paper, a two-stage optimal planning and design method for combined cooling, heat and power (CCHP) microgrid system was presented. The optimal objective was to simultaneously minimize the total net present cost and carbon dioxide emission in life circle. On the first stage, multi-objective genetic algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) was applied to solve the optimal design problem including the optimization of equipment type and capacity. On the second stage, mixed-integer linear programming (MILP) algorithm was used to solve the optimal dispatch problem. The approach was applied to a typical CCHP microgrid system in a hospital as a case study, and the effectiveness of the proposed method was verified. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Combined cooling Heat and power microgrid system Two-stage optimal design method multi-objective genetic algorithm Mixed integer linear programming

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Application of elitist multi-objective genetic algorithm for classification rule generation

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APPLIED SOFT COMPUTING 2008年第1期8卷 477-487页

作者： Dehuri, S. Patnaik, S. Ghosh, A. Mall, R. Fakir Mohan Univ PG Dept Informat & Commun Technol Balasore 756019 Orissa India Indian Stat Inst Machine Intelligence Unit Kolkota W Bengal India Indian Stat Inst Ctr Soft Comp Res Kolkota W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective genetic algorithm with a hybrid crossover operator for optimizing these objectives simultaneously. We have compared our rule discovery procedure with simple genetic algorithm with a weighted sum of all these objectives. The experimental result confirms that our rule discovery algorithm has a clear edge over simple genetic algorithm. (C) 2007 Elsevier B. V. All rights reserved.

关键词： genetic algorithm multi-objective genetic algorithm rule mining data mining

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