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Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm

SOFT COMPUTING 2008年第5期12卷 435-452页

作者： Rahimi-Vahed, Alireza Mirzaei, Ali Hossein Tarbiat Modares Univ Dept Ind Engn Fac Engn Tehran Iran

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm (MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the large-sized problems.

关键词： bi-criteria permutation flow shop scheduling multi-objective shuffled frog-leaping algorithm multi-objective genetic algorithm

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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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Disease Diagnosis with a hybrid method SVR using NSGA-II

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NEUROCOMPUTING 2014年 136卷 14-29页

作者： Zangooei, Mohammad Hossein Habibi, Jafar Alizadehsani, Roohallah Sharif Univ Technol Dept Comp Engn Tehran Iran

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to solve classification problems and vice versa. To regard a classification problem as a regression one, we propose a method based on the Support Vector Regression (SVR) classification model as one of the powerful methods in intelligent field management. We apply the Non-dominated Sorting genetic algorithm-II (NSGA-II), a kind of multi-objective evolutionary algorithm, to find mapping points (MPs) for rounding a real-value to an integer one. Also, we employ the NSGA-II to find out and tune the SVR kernel parameters optimally so as to enhance the performance of our model and achieve better results. The results of the study are compared with the results of some previous studies focusing on the diagnoses of four diseases using the same UCI machine learning database. The experimental results show that the proposed method yields a superior and competitive performance in these four real-world datasets. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Support Vector Regression multi-objective genetic algorithm Disease diagnosis Machine learning

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Smart Platform for Microvibration Control of High-Tech Industry Facilities

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2017年第1期17卷 155-164页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Architectural & Civil Engrg Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

Recently, the microvibrations of high technology facilities resulting from nearby traffic loads have attracted significant attention. Considerable research aimed at protecting high-tech equipment due to nearby road and rail traffic has focused on vibration isolation systems. These isolation systems include passive-base isolation systems, smart base isolation systems, passive mounts, and active tables that are used mainly to isolate a small quantity of high-tech equipment. In this study, a smart platform was developed to control the microvibrations of high-technology facilities. Train-induced ground acceleration was generated as a nearby traffic load and a magnetorheological damper was used to compose a smart isolation platform. A fuzzy logic controller was used as a control algorithm that was optimized using a multi-objective genetic algorithm. The microvibration control performance of a smart isolation platform was compared with that of a conventional passive isolation platform. A series of parametric studies were performed to optimize the design of a passive isolation platform. Numerical analysis showed that a smart isolation platform can effectively control the microvibrations of a high-technology facility subject to train-induced excitation.

关键词： smart platform train-induced ground motion microvibration semi-active control MR damper fuzzy logic controller multi-objective genetic algorithm

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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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Marine shaft optimization using surrogate models and multi-objective optimization

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STRUCTURES 2024年 63卷

作者： Nickabadi, Saeid Hosseini, Seyed Mohammadreza Bathaee, Hasan Alirezaeipour, Saeid Univ Imam Khomeini Marine Sci Fac Mech Engn Nowshahr Iran Univ Tehran Fac Mech Engn Tehran Iran Univ Tarbiat Modares Fac Mech Engn Tehran Iran

This paper aims to obtain an application software using Ansys software in which a static, dynamic, and modal analysis for the power transmission of ships is presented. The objective functions of this research are maximum static stress, maximum dynamic stress, shaft mass, and modal shaft frequency analysis. A complete, comprehensive, and generalizable model for metal shaft types is presented. Then, using the kriging response surface and multi-objective genetic algorithm (NSGA II), these goals are optimized simultaneously. In this research, the optimization parameters are geometric parameters of the shaft design, including the position of the supports and the inner and outer diameters of the shaft. As a result of this optimization, the shaft's maximum static and dynamic stresses are reduced by 36% and 42%, respectively. The shaft's mass, which is one of the most critical factors during the shaft and its vibrations, is reduced by about 9%. In this regard, the first natural frequency of the optimized shaft increased by about 28.5% compared to the initial shaft.

关键词： Ship propulsion shaft Metal shaft Static and dynamic analysis Modal analysis Optimization Kriging response level multi-objective genetic algorithm

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Optimal multi-objective linearized impulsive rendezvous under uncertainty

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ACTA ASTRONAUTICA 2010年第3-4期66卷 439-445页

作者： Li, Hai-yang Luo, Ya-Zhong Jin-Zhang Tang, Guo-Jin Natl Univ Def Technol Coll Aerosp & Mat Engn Changsha 410073 Hunan Peoples R China

The multi-objective optimization of linearized impulsive rendezvous under uncertainty is investigated in this paper. A performance index related to the variances of the terminal state error is defined as the robustness performance index, which is determined by the linear covariance method. The three-objective of the total characteristic velocity, the rendezvous time of flight, and the robustness performance index optimization model based on the Clohessy-Wiltshire (C-W) system is proposed. The multi-objective nondominated sorting genetic algorithm is employed to obtain the Pareto solution set. The optimization results of one homing rendezvous mission are provided, demonstrating that tradeoffs between time of flight, propellant cost and trajectory robustness and several inherent principles of the rendezvous trajectory can be quickly obtained via the current approach. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Rendezvous Uncertainty Robust optimization Linear covariance multi-objective genetic algorithm

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Optimization of aluminium sheet hot stamping process using a multi-objective stochastic approach

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ENGINEERING OPTIMIZATION 2016年第12期48卷 2173-2189页

作者： Xiao, Wenchao Wang, Baoyu Zhou, Jing Ma, Wenyu Yang, Lei Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.

关键词： aluminium hot stamping optimization of process parameters response surface methodology multi-objective genetic algorithm Monte Carlo simulation method

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Enhancement of nutritional value of fried fish using an artificial intelligence approach

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第14期29卷 20048-20063页

作者： Sadhu, Tithli Banerjee, Indrani Lahiri, Sandip Kumar Chakrabarty, Jitamanyu Natl Inst Technol Durgapur Dept Chem Mahatma Gandhi Ave Durgapur 713209 W Bengal India Natl Inst Technol Durgapur Dept Chem Engn Durgapur India

Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140-240 degrees C), times (5-20 min), and oil amounts (25-100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R-2 were 0.99 and 0.98;RMSE were 0.038 and 0.046;and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model-based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 degrees C, 6.06 min, 40 ml oil/kg of fish).

关键词： Artificial neural network genetic algorithm Particle swarm optimization multi-objective genetic algorithm Nutritional value Fish Frying

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Applications of adaptive stiffness suspensions to vibration control of a high-speed stiff rotor with tilting pad bearings

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ARCHIVE OF APPLIED MECHANICS 2021年第4期91卷 1819-1835页

作者： Abbasi, A. Khadem, S. E. Bab, Saeed Tarbiat Modares Univ Dept Mech Engn POB 14115-177 Tehran Iran Niroo Res Inst Mech Rotary Equipment Dept POB 14665-517 Tehran Iran

The current paper discusses the optimum parameter setting of asymmetric high-static low-dynamic stiffness (HSLDS) suspensions to reduce vibrations of a high-speed symmetric rotary system, excited by an unbalance force. The rotating system consists of a shaft that is supported by tilting pad journal bearings on the asymmetric HSLDS suspensions. The Reynolds equation is solved numerically to obtain the oil pressure distribution for each pad of bearing. With the aim of calculating the hydrodynamic forces applied to each pad, an analytical approach is presented. Its results are validated using a numerical integration approach. Given the considerable difference between the shaft mass and pads moment of inertia, the mathematical equations governing the motions of disk, journal, bearing and pads are solved implementing a routine specified for stiff ordinary differential equations in MATLAB. The optimum parameters of HSLDS suspensions are obtained, using a multi-objective genetic algorithm. Design objectives are considered to minimize the vibrations of rotor, journal, and bearing and bearing force transmission to the external supports. The efficiency of optimum HSLDS suspensions in reducing the vibrations of journal, bearing and rotor within the operating speed range is shown. The high performance of the designed suspensions in decreasing the bearings force transmission is proved as well. In addition, the design robustness to uncertainties in HSLDS suspensions parameters is studied.

关键词： High-static low-dynamic stiffness Suspension Rotor Journal Tilting pad journal bearing multi-objective genetic algorithm

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