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Stable cutting zone prediction in CNC turning using adaptive signal processing technique merged with artificial neural network and multi-objective genetic algorithm

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2018年 70卷 238-248页

作者： Shrivastava, Y. Singh, B. Jaypee Univ Engn & Technol Mech Engn Dept Guna MP India

In metal cutting, surface quality and material removal rate are the key parameters investigated by several researchers. It has been already established that, at high-speed machining, tool chatter deteriorates the work-piece surface and effects the material removal rate too. Numerous crucial investigations have been carried out regarding the enhancement of these parameters considering tool chatter as a major thread. In the recent advancement, signal processing techniques are being used for suppression of chatter. Moreover, it has been found these advance techniques helps in predicting the actual nature of chatter. However, the chatter signal recorded during machining usually contain contaminations merged with actual signal. Hence, it becomes a task for researchers to rectify the signal and predict a suitable cutting zone that is capable of obtaining good surface finish with acceptable material removal rate. In the present work, ensemble empirical mode decomposition technique has been used to rectify the signal and optimal cutting zone has been predicted using the artificial neural network and multi-objective genetic algorithm. Machining in the obtained optimal zone will upsurge the productivity, by decreasing tool chatter and increasing material removal rate simultaneously. To validate the proposed methodology, experiments have been performed within the obtained optimal zone. The results indicate the effectiveness of the proposed methodology.

关键词： Tool chatter Data acquisition Ensemble empirical mode decomposition Intrinsic mode function Artificial neural network multi-objective genetic algorithm

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Input optimization of ANFIS typhoon inundation forecast models using a multi-objective genetic algorithm

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JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 2018年 19卷 16-27页

作者： Ouyang, Huei-Tau Natl Ilan Univ 1Sec 1Shennong Rd Yilan 260 Yilan County Taiwan

Inundation forecast models based on a subset of data inputs are advantageous in their efficiency due to the rather fewer inputs needed to be processed. This type of models is nevertheless rare mainly because of the difficulty in selecting the appropriate combination of input variables. In this study, an innovative methodology is proposed to overcome this difficulty by integrating Adaptive Network-based Fuzzy Inference System (ANFIS) and multi objective genetic algorithm (MOGA). The three indices of the coefficient of efficiency (CE), relative peak error (RPE) and relative time shift error (RTS) are used to assess the performances of the models from various perspectives. Optimal combinations of data inputs for the ANFIS models are searched for by MOGA, and the three models with the best performances for each index are selected. Comparisons show that the optimal models obtained by the proposed methodology have better overall performances than the ARX and Nonlinear ARX models. Test results reveal that the optimal models preferably selected for a designated prediction lead time cannot maintain optimality under different prediction leads. This problem has been resolved by the use of a series of models, each optimized with respect to a designated prediction lead time using the same methodology. Test results show that the model series exhibits significant improvements under various prediction leads and thus effectively improves the accuracy of inundation level prediction.

关键词： Optimization ANFIS Model inputs multi-objective genetic algorithm Inundation Typhoon

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Tourists Initial Optimal Shunt Scheme Using multi-objective genetic algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 3517-3527页

作者： Hu, Tao Jin, Maozhu Lei, Xia Liao, Zhixue Ge, Peng Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu Sichuan Peoples R China

With the rapid development of tourism, it not only brings great economic benefits, but also causes some problems. Overcrowded visitors reduce tourists satisfaction and bring about negative impact on ecological environment. Designing a reasonable tourists initial shunt scheme can help the management of the scenic area to achieve the goal that balances the economic development and environment protection. In this paper, for the sake of alleviating congestion in scenic area, a tourists optimal shunt scheme is proposed. Instead of defining single optimal objective, the proposed scheme taking two objectives into account, which are minimizing the total load balance degree of the scenic spots and one spot's load degree. In order to estimate optimal shunt ratios, a multi-objective optimization based on genetic algorithm is used to find the Pareto solution. Then a simulation model is built to investigate and verify the scheme. Finally, a comparison analysis validates the efficiency of the model in mitigating the load of the scenic spots in Jiuzhai Valley.

关键词： Tourist shunt Optimization scheme multi-objective genetic algorithm Simulation

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Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm

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RESOURCES CONSERVATION AND RECYCLING 2018年 128卷 267-283页

作者： Piroozfard, Hamed Wong, Kuan Yew Wong, Wai Peng Univ Teknol Malaysia Fac Mech Engn Dept Mfg & Ind Engn Utm Skudai 81310 Malaysia Univ Sains Malaysia Sch Management George Town 11800 Malaysia

In scheduling, previous research attention has been directed towards classical-based objective functions, while ignoring environmental-based objective functions. The purpose of this research is to present a multi-objective flexible job shop scheduling problem with the objectives of minimizing total carbon footprint and total late work criterion, simultaneously, as sustainability-based and classical-based objective functions, respectively. In order to solve the presented problem effectively, an improved multi-objective genetic algorithm is proposed to obtain high quality non-dominated schedules. This work has three main scientific contributions that are: (1) This is a novel and pioneer research that addresses carbon footprint reduction in flexible job shop scheduling, (2) This is also the first research that addresses the total late work criterion in multi-objective flexible job shop scheduling, and (3) This research proposes an improved multi-objective evolutionary algorithm for solving the newly extended bi-objective problem. Stepwise delineation of the proposed algorithm is provided and fifteen newly extended test instances are solved by the proposed approach. Computational outcomes of the proposed algorithm are compared with two most representative and well-known multi-objective evolutionary algorithms, namely, non-dominated sorting genetic algorithm II and strength Pareto evolutionary algorithm 2. The principal results show that: (1) The proposed algorithm is superior in finding high quality non-dominated schedules, (2) It performs better in four averaged comparison metrics as compared to the other algorithms, and (3) Carbon footprint has an impact on the optimum solutions. As conclusions, the proposed algorithm is useful for production managers to schedule their operations in a way that can reduce carbon emission while minimizing late work. Production managers will also have the flexibility in selecting a schedule from amongst a set of non-dominated schedules.

关键词： Bi-objective flexible job shop scheduling Environmentally sustainable operations management Carbon emission Late work criterion multi-objective genetic algorithm

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Parameters Optimization using Fuzzy Rule Based multi-objective genetic algorithm for an Event Based Rainfall-Runoff Model

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WATER RESOURCES MANAGEMENT 2018年第4期32卷 1501-1516页

作者： Reshma, T. Reddy, K. Venkata Pratap, Deva Agilan, V. Koneru Lakshmaiah Educ Fdn Dept Civil Engn Guntur 522502 Andhra Pradesh India Natl Inst Technol Dept Civil Engn Warangal 506004 Telangana India

The calibration of an event based rainfall-runoff model for steam flow forecasting is challenging because, it is difficult to measure the parameters physically on the field for each rainfall event. In the present study, Fuzzy rule based multi-objective genetic algorithm (MGA) is developed to optimize the infiltration and roughness parameters of an event based rainfall-runoff model. Nash Sutcliffe Efficiency (NSE), Coefficient of Determination (R-2) and transformed volume difference (f(V)) are used as the objective functions of the MGA and all Pareto optimal solutions are identified using Nondominated Sorting method. As three objective functions are included in the calibration, the number of Pareto optimal solutions are also increases and hence, the optimization problem now becomes a decision making problem. Therefore, to select the best solution from all Pareto optimal solutions, a Fuzzy Rule-Based Model (FRBM) is developed to get alternative values of each Pareto optimal solution. First, the Fuzzy rule based MGA is developed by integrating the FRBM with the MGA. Then the Fuzzy rule based MGA is integrated with an event based runoff model. The developed Fuzzy-MGA based runoff model is tested on three different watersheds and the simulation results of Fuzzy-MGA based runoff model are compared with observed data and previous study results. From the simulated events of three watersheds using Fuzzy-MGA based runoff model, it is observed that the mean percentage error in any criteria (i.e. volume of runoff, peak runoff, and time to peak) of the developed model for a watershed is less than 16.33%. It is also noted that the developed Fuzzy-MGA based runoff model is able to produce hydrographs that are much closer to the measured hydrographs.

关键词： Event based rainfall-runoff model Fuzzy Rule-Based Model multi-objective genetic algorithm Optimization

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multi-objective genetic algorithm fractional-order PID controller for semi-active magnetorheologically damped seat suspension

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JOURNAL OF VIBRATION AND CONTROL 2017年第8期23卷 1248-1266页

作者： Gad, S. Metered, H. Bassuiny, A. Ghany, A. M. Abdel Helwan Univ Cairo Egypt

Recently, fractional-order proportional-integral-derivative (FOPID) controllers are demonstrated as a general form of the classical proportional-integral-derivative (PID) using fractional calculus. In FOPID controller, the orders of the derivative and integral portions are not integers which offer more flexibility in succeeding control objectives. This paper proposes a multi-objective genetic algorithm (MOGA) to optimize the FOPID controller gains to enhance the ride comfort of heavy vehicles. The usage of magnetorheological (MR) damper in seat suspension system provides considerable benefits in this area. The proposed semi-active control algorithm consists of a system controller that determines the desired damping force using a FOPID controller tuned using a MOGA, and a continuous state damper controller that calculates the input voltage to the damper coil. A mathematical model of a six degrees-of-freedom seat suspension system incorporating human body model using an MR damper is derived and simulated using Matlab/Simulink software. The proposed semi-active MR seat suspension is compared to the classical PID, optimum PID tuned using genetic algorithm (GA) and passive seat suspension systems for predetermined chassis displacement. System performance criteria are examined in both time and frequency domains, in order to verify the success of the proposed FOPID algorithm. The simulation results prove that the proposed FOPID controller of MR seat suspension offers a superior performance of the ride comfort over the integer controllers.

关键词： Vibration control ride comfort semi-active suspension seat suspension MR damper FOPID controller multi-objective genetic algorithm continuous state controller human body model

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Satellite scheduling of large areal tasks for rapid response to natural disaster using a multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2018年 28卷 813-825页

作者： Nin, Xiaonan Tang, Hong Wu, Lixin Beijing Normal Univ Fac Geog Sci Minist Educ Key Lab Environm Change & Nat Disaster Beijing 100875 Peoples R China Beijing Normal Univ Beijing Key Lab Environm Remote Sensing & Digital Beijing 100875 Peoples R China Cent S Univ Sch Geosci & Infophys Changsha 410083 Hunan Peoples R China

Earth satellite observations are very useful during the response phase of disaster management, since satellites could provide accurate, frequent and almost instantaneous data for large areas anywhere in the world. To rapidly respond to natural disasters, a key problem is how to efficiently schedule multiple earth observation satellites to acquire image data of a large stricken area by coordinating multiple different even conflicting needs of disaster relief, such as the extent of coverage over the stricken area, timeliness, and the spatial resolution. In this paper, considering two typical application scenarios during the response phase, we propose a multi-objective optimization method to solve the problem of satellite scheduling of a large area target. First, we design a decomposition method to partition a areal task into a series of observation strips. Next, the multiple satellite tasking problem is expressed as a multi-objective integer-programming model including optimizing objectives of the coverage rate, the imaging completion time, the average spatial resolution and the average slewing angle. Finally, the multi-objective genetic algorithm NSGA-II is designed to obtain optimal solutions of satellite scheduling. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of satellite image acquisition in the context of emergency response. To prove the advantage of NSGA-II, a comparison with state-of-the-art approaches is performed. Furthermore, we discuss the applicability of the proposed method under two kinds of situations: (1) roughly grasping the damage of affected area as soon as possible and (2) accurately assessing the damage of buildings in the worst-hit area.

关键词： Disaster emergency response Area target Decomposition multi-satellite scheduling multi-objective genetic algorithm

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Design of a Data-Driven Control System using a multi-objective genetic algorithm

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IFAC-PapersOnLine 2019年第29期52卷 310-313页

作者： Takuya Kinoshita Toru Yamamoto Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima Japan

In recent years, control design schemes for directly calculating control parameters from operational data have been realized and include the virtual reference feedback tuning (VRFT) method and the fictitious reference iterative tuning (FRIT) method. They were designed for objects that have a linear system. However, many objects in industry are nonlinear; hence, it is challenging to obtain good control performance by only applying fixed PID controllers. In this study, multiple linear systems as objects using multiple linear controllers are investigated. Specifically, it is necessary to solve two optimization problems of (i) the number of controllers (ii) the control parameters of each controller, and it is solving by using multi-objective genetic algorithm (MOGA) in this research.

关键词： multi-objective genetic algorithm data-driven control VRFT

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An Approach for Optimizing Group Stock Portfolio Using multi-objective genetic algorithm 5

An Approach for Optimizing Group Stock Portfolio Using Multi...

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5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)

作者： Chen, Chun-Hao Chiang, Bing-Yang Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Sun Yat Sen Univ Comp Sci & Engn Kaohsiung Taiwan Natl Kaohsiung Univ Comp Sci & Informat Engn Kaohsiung Taiwan

ISBN: (纸本)9781728102078

Stock portfolio optimization is always an attractive research topic because of the variety of financial markers. In the past decade, lots of approaches have been proposed to deal with the problem. Previously, an algorithm was also designed for the group stock portfolio optimization problem. In that approach, portfolio satisfaction and group balance are utilized to evaluate the goodness of possible solutions. However, there is trade-off between those factors. In this paper, we thus design an approach for finding group stock group portfolios using the multi-objective genetic algorithm. Two objective functions, Sharpe ratio and group balance, are employed to derive possible Pareto solutions. Experiments on a real dataset were conducted to verity the effectiveness of the proposed approach.

关键词： Portfolio optimization group stock portfolio genetic algorithm grouping genetic algorithm multi-objective genetic algorithm

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Gene Ranking: A Novel Approach Using multi-objective genetic algorithm 7

Gene Ranking: A Novel Approach Using Multi-Objective Genetic...

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7th International Conference on Reliability, Infocom Technologies and Optimization - Trends and Future Directions (ICRITO)

作者： Das, Priyojit Saha, Sujay Ghosh, Anupam Dey, Kashi Nath Natl Inst Technol Calicut Dept Comp Sci & Engn Kozhikode 673601 Kerala India Heritage Inst Technol Dept Comp Sci & Engn Kolkata 700107 W Bengal India Netaji Subhas Engn Coll Dept Comp Sci & Engn Kolkata 700152 W Bengal India Univ Calcutta Dept Comp Sci & Engn Kolkata 700009 W Bengal India

ISBN: (纸本)9781538646922

In a Gene Co-expression Network, the same or closely related genes are clustered into co-expressed groups. It is necessary to investigate the role that these genes play as far as some complex diseases like cancer are concerned in those networks. Ranking those genes actually discover the significant candidate genes for various types of cancers. There are several gene ranking algorithms proposed so far that produces the top ranked genes according to their importance with respect to a particular cancer disease. In this work, we apply multi-objective genetic algorithm, multi-objective Network GA, on a gene coexpression network to find the top ranked cancer mediating genes. The algorithm is applied to publicly available real-life cancer datasets taken from NCBI (National Centre for Biotechnology Information) biological online repository. The performance of the algorithm is justified by classification using SVM classifier with linear kernel and it is compared with state-of-the-art methods on the basis of percentage of accuracy, precision, recall, and F1-Score.

关键词： Microarray Gene Expression Data genetic algorithm multi-objective genetic algorithm PageRank HITS

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