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

JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 2018年第Mar.期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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Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China

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SOLAR ENERGY 2016年 132卷 38-50页

作者： Zhang, Longwei Zhang, Lingling Wang, Yuetao Chinese Acad Sci Inst Appl Ecol Shenyang 110164 Peoples R China Shenyang Jianzhu Univ Sch Architecture & Urban Design Shenyang 110168 Liaoning Provin Peoples R China Shandong Jianzhu Univ Coll Architecture & Urban Planning Jinan 250101 Shandong Peoples R China

In the severe cold zones of China, solar radiation is one of the most important issues in architectural design. The design seeks to make buildings receive more direct sunlight within the limits of the user's comfort and simultaneously save energy and space. So far in China, the design of solar radiation has usually been qualitative, not quantitative, and it is often implemented by architects with experience or those following convention. This rough and rigid design approach is not accurate or efficient, particularly in the design of free-form buildings, which comprise a class of irregular-form buildings popular in current architectural design. Moreover, solar radiation is not the only thing that needs to be considered;shape coefficient and space efficiency should also be considered in free-form building design. This study proposes a method for a free-form building that receives more solar radiation though shape optimization and takes into account the other two objectives mentioned above. This paper provides a method with a "Modeling-simulation-Optimization" framework. In the process of applying this method, parametric modeling with Rhinoceros and Grasshopper is used to build up the free-form building model, and the shape optimization of the building is processed by using the multi-objective genetic algorithm to make sure the three objectives-i.e., to maximize solar radiation gain, to maximize space efficiency, and to minimize the shape coefficient-are all achieved. Finally, a Pareto frontier is generated to show the optimal solutions and to assist designers in making final decisions. The case study shows that compared with the cube-shaped reference building, the total solar radiation gain of the optimized free-form shape building is 30-53% higher, and the shape coefficient value is reduced by 15-20%, with a decrease of less than 5% of the space efficiency values. The proposed method, according to the basic process of architecture design, uses a performance-driven ap

关键词： Free-form building Solar radiation gain Shape optimization multi-objective genetic algorithm Severe cold zone

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A combination of multi-objective genetic algorithm and deep learning for music harmony generation

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multiMEDIA TOOLS AND APPLICATIONS 2023年第2期82卷 2419-2435页

作者： Majidi, Maryam Toroghi, Rahil Mahdian Iran Broadcasting Univ Fac Media Technol & Engn Tehran Iran

Automatic Music Generation (AMG) has become an interesting research topic for many scientists in artificial intelligence, who are also interested in the music industry. One of the main challenges in Automatic Music Generation is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction. Also, original music contains different elements that should work together, such as melody, harmony, and rhythm;but in the most of previous works, Automatic Music Generation works only for one element (e.g., melody). Therefore, in this paper, we propose a multi-objective genetic algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory;and the other two objective functions are modeled scores provided by music experts and ordinary listeners. The scoring of experts and listeners separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks. The proposed music generation system tries to maximize mentioned objective functions to generate a new piece of music, including melody and harmony. The results show that the proposed method can generate pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar.

关键词： Automatic Music generation Polyphonic Music pieces Harmony multi-objective genetic algorithm Bi-LSTM

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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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RF design of APEX2 two-cell continuous-wave normal conducting photoelectron gun cavity based on multi-objective genetic algorithm

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2019年 940卷 12-18页

作者： Luo, T. Feng, H. Filippetto, D. Johnson, M. Lambert, A. Li, D. Mitchell, C. Sannibale, F. Staples, J. Virostek, S. Wells, R. Lawrence Berkeley Natl Lab One Cyclotron Rd Berkeley CA 94720 USA Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China

High brightness, high repetition rate electron beams are key components for optimizing the performance of next generation scientific instruments, such as MHz-class X-ray Free Electron Laser (XFEL) and Ultra-fast Electron Diffraction/Microscopy (UED/UEM). In the Advanced Photo-injector EXperiment (APEX) at Berkeley Lab, a photoelectron gun based on a 185.7 MHz normal conducting re-entrant RF cavity, has been proven to be a feasible solution to provide high brightness, high repetition rate electron beam for both XFEL and UED/UEM. Based on the success of APEX, a new electron gun system, named APEX2, has been under development to further improve the electron beam brightness. For APEX2, we have designed a new 162.5 MHz two-cell photoelectron gun and achieved a significant increase on the cathode launching field and the beam exit energy. For a fixed charge per bunch, these improvements will allow for the emittance reduction and hence to an increased beam brightness. The design of APEX2 gun cavity is a complex problem with multiple design goals and restrictions, some even competing each other. For a systematic and comprehensive search for the optimized cavity geometry, we have developed and implemented a novel optimization method based on the multi-objective genetic algorithm (MOGA).

关键词： Photoelectron RF gun RF cavity design multi-objective genetic algorithm

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Optimal placement of piezoelectric actuators and sensors on a smart beam and a smart plate using multi-objective genetic algorithm

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SMART STRUCTURES AND SYSTEMS 2015年第4期15卷 1041-1062页

作者： Nestorovic, Tamara Trajkov, Miroslav Garmabi, Seyedmehdi Ruhr Univ Bochum Mech Adapt Syst D-44801 Bochum Germany

In this paper a method of fmding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and H-infinity norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

关键词： optimal location smart structures H-infinity norm genetic algorithm multi-objective genetic algorithm

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Workload Allocation in IoT-Fog-Cloud Architecture Using a multi-objective genetic algorithm

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JOURNAL OF GRID COMPUTING 2020年第1期18卷 43-56页

作者： Abbasi, Mahdi Pasand, Ehsan Mohammadi Khosravi, Mohammad R. Bu Ali Sina Univ Engn Fac Dept Comp Engn Hamadan Hamadan Iran Persian Gulf Univ Dept Comp Engn Bushehr Iran Shiraz Univ Technol Dept Elect & Elect Engn Telecommun Grp Shiraz Iran

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been considerably increased. The processing resources of IoT nodes cannot cope with such huge workloads. Processing parts of the workload in clouds could solve this problem, but the quality of services for end-users will be decreased. Given the latency reduction for end-users, the concept of processing in the fog devices, which are at the edge of the network has been evolved. Optimizing the energy consumption of fog devices in comparison with cloud devices is a significant challenge. On the other hand, providing the expected-quality of service in processing the requested workloads is highly dependent on the propagation delay between fog devices and clouds, which due to the nature of the distribution of clouds with the different workloads, is highly variable. To date, none of the proposed solutions has solved the problem of workload allocation given the criteria of minimizing the energy and delay of fog devices and clouds, simultaneously. This paper presents a processing model for the problem in which a trade-off between energy consumption and delay in processing workloads in fog is formulated. This multi-objective model of the problem is solved using NSGAII algorithm. The numerical results show that by using the proposed algorithm for workload allocation in a fog-cloud scenario, both of energy-consumption and delay can be improved. Also, by allocating 25% of the IoT workloads to fog devices, the energy consumption and delay are both minimized.

关键词： Fog computing Internet of Things multi-objective genetic algorithm Workload distribution Energy Delay

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Traffic accident severity prediction using a novel multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF CRASHWORTHINESS 2017年第4期22卷 425-440页

作者： Hashmienejad, Seyed Hessam-Allah Hasheminejad, Seyed Mohammad Hossein Razi Univ Dept Civil Engn Kermanshah Iran Alzahra Univ Dept Comp Engn Tehran Iran

Prediction of traffic accident severity is a motor vehicle traffic challenge due to its impact on saving human lives. There are several researches in the literature to predict traffic accident severity based on artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs) and other classification methods. In fact, the main disadvantage of ANNs and SVMs is lack of interpretation for human and the main disadvantage of classical DTs such as C4.5, ID3 and CART is their low accuracy. To address these drawbacks, in this paper we propose a novel rule-based method to predict traffic accident severity according to user's preferences instead of conventional DTs. In the proposed method, we customised a multi-objective genetic algorithm, i.e. Non-Dominated Sorting genetic algorithm (NSGA-II), to optimise and identify rules according to Support, Confidence and Comprehensibility metrics. The goal of the proposed method is providing facilities to make use of the knowledge of users, including traffic police, roads and transportation engineers and trade-off among all the conflicting objectives. The proposed method is evaluated by a traffic accident data set including 14211 accidents in rural and urban roads in Tehran Province of Iran for a period of 5years (2008-2013). The evaluation results revealed that the proposed method outperforms the classification methods such as ANN, SVM, and conventional DTs according to classification metrics like accuracy (88.2%), and performance metrics of rules like support and confidence (0.79 and 0.74, respectively).

关键词： Traffic accident severity prediction multi-objective genetic algorithm decision trees Non-Dominated Sorting genetic algorithm

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Optimal positioning of wind turbines on Gokceada using multi-objective genetic algorithm

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WIND ENERGY 2010年第4期13卷 297-306页

作者： Sisbot, Sedat Turgut, Oezgue Tunc, Murat Camdali, Uenal Abant Izzet Baysal Univ Fac Engn & Architecture Dept Mech Engn TR-14280 Bolu Turkey Yeditepe Univ Fac Engn Dept Syst Engn TR-34755 Istanbul Turkey

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokceada Island located at the north-east of Aegean Sea bearing full potential of wind energy generation. A multi-objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： wind farms multi-objective genetic algorithm optimum localization Gokceada Island

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Configuration optimization of shell-and-tube heat exchangers with helical baffles using multi-objective genetic algorithm based on fluid-structure interaction

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2017年 85卷 62-69页

作者： Wang, Simin Xiao, Juan Wang, Jiarui Jian, Guanping Wen, Jian Zhang, Zaoxiao Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

The configuration parameters of helical angle and overlapped degree of shell-and-tube heat exchangers with helical baffles have been discussed for the thermal-structural comprehensive performance. Based on fluid structure interaction theory, a method on configuration optimization of shell-and-tube heat exchangers with helical baffles is introduced using second-order polynomial regression response surface combined with multi objective genetic algorithm. The results show that the heat transfer coefficient per unit pressure drop of shell and -tube heat exchangers with helical baffles increases firstly and then decreases with the increase of helical angle, and decreases with the increase of overlapped degree under certain shell-inlet velocity. And the performance of flow and heat transfer is more sensitive to helical angle compared with overlapped degree. The maximum shear stress increases with helical angle, but it is almost unaffected by overlapped degree for mechanical properties of helical baffles. The objectives of optimization are the heat transfer coefficient per unit pressure drop maximizing and maximum shear stress minimizing with scope of allowable stress, and three optimal structures are obtained. The optimal results indicate that the heat transfer coefficient per unit pressure drop increases averagely by 14.1%, the maximum shear stress decreases averagely by 4.1%, which provides theoretical guidance for industrial design of shell-and-tube heat exchangers with helical baffles.

关键词： Shell-and-tube heat exchangers with helical baffles Fluid-structure interaction multi-objective genetic algorithm Configuration optimization Mechanical properties

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