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multi-item Supplier Selection Model with Fuzzy Risk Analysis Studied by Possibility and Necessity Constraints

FUZZY INFORMATION AND ENGINEERING 2015年第4期7卷 451-474页

作者： Patra, Kartik Mondal, Shyamal Kumar Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 WB India

Three different supplier selection models have been developed in crisp and fuzzy environments. Here two objective functions have been considered, profit and risk. In this paper, profit has been maximized and risk has been minimized with some constraints. Each supplier has an limited capacity. The purchasing cost of each item from different supplier as well as associative risk is known. The total space and budget of a retailer are constant. In Model I, all the parameters are considered as crisp. In Model II, the demand has been considered as fuzzy. In Model III, the risk values and demand have been considered as fuzzy. To defuzzyfy the fuzzy constraints, necessity and possibility have been introduced. To defuzzyfy the fuzzy objective, two different methods, credibility measure and alpha-cut method have been introduced. All the models have been illustrated numerically using multi-objective genetic algorithm (MOGA). Also a sensitivity analysis has been done taking different sets of risk values and a comparison result has been shown for credibility measure and alpha-cut-method-for Model-III.

关键词： Supplier selection Necessity Possibility Risk multi-objective genetic algorithm

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Evolutionary feature selection for emotion recognition in multilingual speech analysis

Evolutionary feature selection for emotion recognition in mu...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Brester, Christina Semenkin, Eugene Kovalev, Igor Zelenkov, Pavel Sidorov, Maxim Siberian State Aerosp Univ Inst Comp Sci & Telecommun Krasnoyarsk Russia Univ Ulm Inst Commun Engn Ulm Germany

ISBN: (纸本)9781479974924

In the case when conventional feature selection methods do not demonstrate sufficient performance, alternative algorithmic schemes might be applied. In this paper we propose an evolutionary feature selection technique based on the two-criteria optimization model. To diminish the drawbacks of genetic algorithms, which are used as optimizers, we design a parallel multi-criteria heuristic procedure based on an island model. The effectiveness of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the crucial aspects in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were engaged in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 11.15% relative improvement compared with the best F-score value on the full set of attributes).

关键词： feature selection multi-objective genetic algorithm island model emotion recognition

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multi-objective optimization of PID controller for temperature control in centrifugal machines using genetic algorithm

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Research Journal of Applied Sciences, Engineering and Technology 2014年第9期7卷 1794-1802页

作者： Singh, Sanjay Kr. Boolchandani, D. Modani, S.G. Katal, Nitish Department of ECE Anand International College of Engineering Jaipur Rajasthan 303012 India Malaviya National Institute of Technology Jaipur Rajasthan 302017 India Department of Electronics and Communication Engineering Amity University Rajasthan 302001 India

The main aim of the study focuses on the optimizing the response of the PID controllers used typically for temperature control loop in centrifugal machines in sugar industry using soft-computing. The centrifugal machines are used for the filtering sugar and molasses and the whole process is carried out at a certain fixed temperature. Any alterations from the set-point will cause instability in the system resulting in unsafe process conditions, poor product quality, unnecessary plant shutdowns, higher maintenance and operating costs, etc. This study employs the optimization of PID controllers using multi-objective genetic algorithm for better plant operations. For the initial tuning of the PID controllers, classical methods like Ziegler-Nichols, Chien-Hrones-Reswick (CHR) and robust time response have been used, but all have shown the transient response;so MATLAB's PID Tuner has been used for the initial estimation of the parameters followed by optimization using genetic algorithm and multi-objective genetic algorithm. On comparing the results, better results have been obtained in case of multi-objective genetic algorithm optimization offering better plant operation and process safety which classical methods have failed to provide. © Maxwell Scientific Organization, 2014.

关键词： Controller tuning genetic algorithm multi-objective genetic algorithm PID controllers PID optimization Robust time response Temperature control

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Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1756-1766页

作者： Jia, Jie Chen, Jian Chang, Guiran Tan, Zhenhua Northeastern Univ Coll Informat Sci & Engn 402 Sch Informat Sci & Engn Shenyang 110004 Liaoning Prov Peoples R China Northeastern Univ Ctr Comp Shenyang 110004 Peoples R China Northeastern Univ Sch Software Shenyang 110004 Peoples R China

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Coverage control Energy multi-objective genetic algorithm Wireless sensor network

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Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

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ENVIRONMENTAL MODELLING & SOFTWARE 2009年第4期24卷 530-541页

作者： Behzadian, Kourosh Kapelan, Zoran Savic, Dragan Ardeshir, Abdollah Univ Exeter Ctr Water Syst Exeter EX4 4QF Devon England Amir Kabir Univ Technol Environm Res Ctr Tehran Iran Amir Kabir Univ Technol Dept Civil & Environm Engn Tehran Iran

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA-ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the 'full' fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA-ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA-ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Water distribution system Calibration Uncertainty multi-objective genetic algorithm Adaptive neural networks

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Springback control of sheet metal forming based on the response-surface method and multi-objective genetic algorithm

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MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2009年第1-2期499卷 325-328页

作者： Liu Wei Yang Yuying Xing Zhongwen Zhao Lihong Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

An automated optimization method by the integration of the response-surface method and an optimization algorithm is presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, a multi-objective genetic algorithm is applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. The response-surface model is employed as a fast analysis tool to surrogate the time-consuming finite-element procedure in the iterations of the multi-objective genetic algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is more efficient than the traditional manual finite-element procedure and the 'trial and error' approach for springback controlling. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Springback Sheet metal Response-surface method multi-objective genetic algorithm

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Thermo-economic triple-objective optimization of a solar chimney power plant using genetic algorithms

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ENERGY 2014年 70卷 204-211页

作者： Gholamalizadeh, Ehsan Kim, Man-Hoe Kyungpook Natl Univ Sch Mech Engn Taegu South Korea

A triple-objective design method was developed for a solar chimney power plant system that simultaneously optimizes the expenditure, total efficiency, and power output. A multi-objective genetic algorithm was used to obtain the best combination of geometric parameters of the power plant. The following design parameters were selected: collector radius, chimney height, and chimney diameter. Two different solar chimney power plant configurations were considered: the Kerman pilot power plant and Manzanares prototype power plant. A set of possible optimal solutions (Pareto optimal set) was obtained. Based on the optimal solutions, the best configuration for each power plant was selected. The performance and expenditure of the optimal solutions and the built power plants were compared. The results showed that the increment of the power output was higher than the increment of the expenditure in the optimal configuration. A parametric study was conducted to evaluate the effects of changing design parameters on different objective functions. This paper provides a very useful design and optimization methodology for solar chimney power plant systems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar chimney power plant Renewable energy Thermo-economic analysis Optimization multi-objective genetic algorithm

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An improved cellular automata model of enzyme kinetics based on genetic algorithm

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CHEMICAL ENGINEERING SCIENCE 2014年 110卷 105-118页

作者： Kar, Saurajyoti Nag, Kaustuv Dutta, Abhishek Constales, Denis Pal, Tandra Natl Inst Technol Dept Biotechnol Durgapur 713209 W Bengal India Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 W Bengal India Associatie KU Leuven Dept Chem Engn Grp Leuven Engn Coll T B-3000 Louvain Belgium Univ Ghent Dept Math Anal B-9000 Ghent Belgium

Based on a generalized Cellular Automata (CA) model which has the flexibility of reaction interactions between all possible two-agent combinations on both reactant and product side, in previous studies by the authors, probabilistic reaction events were combined in a multiplicative order and an exploratory study of the variability analysis was performed. However it was found that the probabilistic parameter values are extremely sensitive to the final model output. As such, NSGA-II based on a multi-objective genetic algorithm (MOGA) is used in this study to obtain optimum sets of these probability parameter values within a feasible computational time. For each generation of the MOGA, the probability rules parameter values are updated (improved) based on the objective functions. Finally, a validation study is performed to indicate the applicability of the CA model to represent specific enzymatic reactions when coupled with the multi-objective optimization algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Cellular automata multi-objective genetic algorithm NSGA-II Enzyme kinetics

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Proportional integral controller gain tuning using real time digital simulation models and multi-objective optimization based co-simulation

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IFAC-PapersOnLine 2015年第30期48卷 473-478页

作者： Kumaraguru Prabakar Fangxing Li The University of Tennessee Knoxville

Proportional integral (PI) controller is a widely used controller strategy. It has two gain constants: proportional(k p ) and integral(k i ) gain constant. The dynamics of the PI controller depends on the values of the gain constant used. If gains of these control terms are not selected properly, instability will occur. Gain tuning methodologies are available to tune these controllers to avoid instability. But, these gain tuning strategies and algorithms utilize simplified mathematical model of the plant in order to obtain gain values. This approach might not provide optimal gain values for models used in real time digital simulator. In the proposed approach, the algorithm utilizes the step response of the system to obtain optimal gain constants. The plant is modeled using the real time digital simulator (RTDS). A multi-objective optimization formulation is used to find the optimal gain constants. Results are presented here to support the effectiveness of proposed multi-objective gain tuning strategy.

关键词： Proportional integral control Gain tuning multi-objective optimization multi-objective genetic algorithm

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Efficient multi-objective genetic algorithm for hardware-software partitioning in embedded system design: ENGA

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2009年第3-4期36卷 181-190页

作者： Jagadeeswari, M. Bhuvaneswari, M. C. Sri Ramakrishna Engn Coll Dept Elect & Commun Engn Coimbatore 641022 Tamil Nadu India P S G Coll Technol Dept Elect & Elect Engn Coimbatore 641004 Tamil Nadu India

This paper presents a novel multi-objective evolutionary algorithm for hardware software partitioning of embedded systems. Customised genetic algorithms have been effectively used for solving complex optimisation problems (NP Hard) but are mainly applied to optimise a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objective functions like area, performance, power, latency, etc., which are to be maximised or minimised at the early stage of the design process. Hence multi- objective formulations are realistic models for many complex engineering optimisation problems. A multi-objective optimisation problem usually has a set of Pareto-optimal solutions, instead of one single optimal solution. A method is put forward for generating Pareto solutions using elitist non-dominated sorting genetic algorithm (ENGA) whose complexity is only O(MN2), where M is the number of objectives and N is the population size. The algorithm is implemented using Visual C++ and the performance metrics for weighted-sum genetic algorithm (WSGA) and ENGA are compared. The results of extensive hardware/software partitioning technique on numerous benchmarks are also presented which can be used practically at the early stage of the design process. From the simulation results ENGA (NSGA-II) was found to perform better than WSGA.

关键词： hardware-software partitioning embedded systems multi-objective genetic algorithm elitist non-dominated sorting genetic algorithm ENGA (NSGA-II) Pareto-optimal solutions

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