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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Wen, Xiaoyu Li, Xinyu Gao, Liang Wan, Liang Wang, Wenwen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781467363433

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multiobjective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

关键词： Integrated process planning and scheduling multi-objective genetic algorithm fuzzy processing time

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multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility

Multi-objective genetic algorithm for the automated planning...

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Conference on Sensors and Command, Control, Communications and Intelligence (C31) Technologies for Homeland Security and Homeland Defense III

作者： Jourdan, DB de Weck, OL MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (纸本)0819453269

This paper examines the optimal placement of nodes for a Wireless Sensor Network (WSN) designed to monitor a critical facility in a hostile region. The sensors are dropped from an aircraft, and they must be connected (directly or via hops) to a High Energy Communication Node (HECN), which serves as a relay from the ground to a satellite or a high-altitude aircraft. The sensors are assumed to have fixed communication and sensing ranges. The facility is modeled as circular and served by two roads. This simple model is used to benchmark the performance of the optimizer (a multi-objective genetic algorithm, or MOGA) in creating WSN designs that provide clear assessments of movements in and out of the facility, while minimizing both the likelihood of sensors being discovered and the number of sensors to be dropped. The algorithm is also tested on two other scenarios;in the first one the WSN must detect movements in and out of a circular area, and in the second one it must cover uniformly a square region. The MOGA is shown again to perform well on those scenarios, which shows its flexibility and possible application to more complex mission scenarios with multiple and diverse targets of observation.

关键词： multi-objective genetic algorithm wireless sensor network network planning node placement facility monitor coverage

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multi-objective genetic algorithm optimization of artificial neural network for estimating suspended sediment yield in Mahanadi River basin, India

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INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT 2020年第2期18卷 207-215页

作者： Yadav, Arvind Satyannarayana, Penke Koneru Lakshmaiah Educ Fdn Dept Elect & Comp Engn Vaddeswaram 522502 AP India Natl Inst Technol Dept Min Engn Rourkela India

Rivers are an integral part of the hydrological cycle and are the major geological agents which erode the continents and transport water and sediments to the oceans. Thus rivers act an important link between continents and oceans for the mass balance. Estimating the suspended sediment yield is one of the crucial aims in the field of managing, designing and planning of any river system or reservoir. To determine the suspended sediment yield in a river basin demands more labour or it is more costly when automatic sampling devices are used. The number of variables and the inter-relationship existing among them influence the suspended sediment yield and the nature of these inter-relationships is neither linear nor simple. Unfortunately, it is a difficult task to determine the suspended sediment yield when traditional mathematical models are used as complex variables and processes are involved. The major key factors, such as basin geology (rock type), relief, rainfall, temperature, water discharge and catchment area that affect sediment yield, are used as inputs to develop the model for predicting the suspended sediment yield in the Mahanadi River. In this paper, a multi-objective genetic algorithm for artificial neural network (MOGA-ANN)-based approach is used for predicting the suspended sediment yield. The MOGA assists ANN to minimize the two competing objectives, i.e. mean error and variance simultaneously. Thus in this study, a hybrid artificial intelligence-based method, MOGA-ANN model, is developed using the hydro-geological-climatic factors where all parameters associated with the ANN models are optimized simultaneously using MOGAs to estimate the suspended sediment yield in the Mahanadi River basin. The ANN's parameters are optimized globally by the MOGA to accurate estimation. The study has been carried out to develop MOGA-ANN for estimating the suspended sediment load using 20-year data at the Tikarapara gauging station which is the last downstream station in

关键词： Artificial neural network multi-objective genetic algorithm water discharge suspended sediment yield Mahanadi River

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multi-objective genetic algorithm based clustering approach and its application to gene expression data

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3rd International Conference on Advances in Information Systems

作者： Özyer, T Liu, YM Alhajj, R Barker, K Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

ISBN: (纸本)3540234780

Gene clustering is a common methodology for analyzing similar data based on expression trajectories. Clustering algorithms in general need the number of clusters as a priori, and this is mostly hard to estimate, even by domain experts. In this paper, we use Niched Pareto k-means genetic algorithm (GA) for clustering m-RNA data. After running the multi-objective GA, we get the pareto-optimal front that gives alternatives for the optimal number of clusters as a solution set. We analyze the clustering results under two cluster validity techniques commonly cited in the literature, namely DB index and SD index. This gives an idea about ranking the optimal numbers of clusters for each validity index. We tested the proposed clustering approach by conducting experiments using three data sets, namely figure2data, cancer (NC160) and Leukaemia data. The obtained results are promising;they demonstrate the applicability and effectiveness of the proposed approach.

关键词： multi-objective genetic algorithm clustering validity analysis gene expression data analysis

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Hydraulic Optimization of a Double-channel Pump's Impeller Based on multi-objective genetic algorithm

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Chinese Journal of Mechanical Engineering 2015年第3期28卷 634-640页

作者： ZHAO Binjuan WANG Yu CHEN Huilong QIU Jing HOU Duohua School of Energy and Power Engineering Jiangsu University Shanghai Kaiquan Pump (Goup) Co. Ltd

Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vort

关键词： double-channel pump＇s impeller multi-objective genetic algorithm artificial neural network computational fluid dynamics（CFD） uni

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multiple sequence alignment with affine gap by using multi-objective genetic algorithm

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014年第1期114卷 38-49页

作者： Kaya, Mehmet Sarhan, Abdullah Alhajj, Reda Firat Univ Dept Comp Engn TR-23119 Elazig Turkey Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Global Univ Dept Comp Sci Beirut Lebanon

multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate and statistically significant multiple alignments is still a challenge. In this paper, we propose an efficient method by using multi-objective genetic algorithm (MSAGMOGA) to discover optimal alignments with affine gap in multiple sequence data. The main advantage of our approach is that a large number of tradeoff (i. e., non-dominated) alignments can be obtained by a single run with respect to conflicting objectives: affine gap penalty minimization and similarity and support maximization. To the best of our knowledge, this is the first effort with three objectives in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding alignments. By analyzing the obtained optimal alignments, the decision maker can understand the tradeoff between the objectives. We compared our method with the three well known multiple sequence alignment methods, MUSCLE, SAGA and MSA-GA. As the first of them is a progressive method, and the other two are based on evolutionary algorithms. Experiments on the BAliBASE 2.0 database were conducted and the results confirm that MSAGMOGA obtains the results with better accuracy statistical significance compared with the three well-known methods in aligning multiple sequence alignment with affine gap. The proposed method also finds solutions faster than the other evolutionary approaches mentioned above. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

关键词： multiple sequence alignment multi-objective genetic algorithm Bioinformatics

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Applications of network analysis and multi-objective genetic algorithm for selecting optimal water quality sensor locations in water distribution networks

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第7期19卷 2333-2344页

作者： Yoo, Do Guen Chung, Gunhui Sadollah, Ali Kim, Joong Hoon Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Hoseo Univ Dept Civil Engn Asan 336795 South Korea

In this study, optimal water quality sensor placement is performed based on the sensitivity of flow direction under different water demands for detecting accidental water quality contamination. First, Betweenness Centrality (BC), a network analysis method, is used for determining optimal locations considering a network's connectivity. Second, sensor locations are optimized for minimizing the contaminant intrusion detection time using the travel time matrix and the multi-objective genetic algorithm (MOGA). These methods were applied to two water distribution networks. It was found that the BC method generates optimal locations close to the water sources and the water main, whereas the MOGA-based method generates optimal sensor locations far away from the sources. These results support the following conclusions. First, the installation priority of gauges can be determined with a more objective standard using the aforementioned two methods. Second, given specific objectives, the two models can be used as alternative decision-making tools for sensor installation.

关键词： water distribution system optimized sensors placement sensitivity of flow direction betweenness centrality multi-objective genetic algorithm

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Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm

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NEURAL COMPUTING & APPLICATIONS 2012年第6期21卷 1281-1295页

作者： Loghmanian, Sayed Mohammad Reza Jamaluddin, Hishamuddin Ahmad, Robiah Yusof, Rubiyah Khalid, Marzuki Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Fac Mech Engn Dept Appl Mech Johor Baharu 81310 Malaysia

The problem of constructing an adequate and parsimonious neural network topology for modeling non-linear dynamic system is studied and investigated. Neural networks have been shown to perform function approximation and represent dynamic systems. The network structures are usually guessed or selected in accordance with the designer's prior knowledge. However, the multiplicity of the model parameters makes it troublesome to get an optimum structure. In this paper, an alternative algorithm based on a multi-objective optimization algorithm is proposed. The developed neural network model should fulfil two criteria or objectives namely good predictive accuracy and minimum model structure. The result shows that the proposed algorithm is able to identify simulated examples correctly, and identifies the adequate model for real process data based on a set of solutions called the Pareto optimal set, from which the best network can be selected.

关键词： Artificial neural network multi-objective genetic algorithm NSGA-II System identification Model structure selection

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GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

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APPLIED SOFT COMPUTING 2019年 79卷 310-325页

作者： Harkat, H. Ruano, A. E. Ruano, M. G. Bennani, S. D. Univ Algarve Fac Sci & Technol Faro Portugal Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Fes Morocco Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Univ Coimbra CISUC Coimbra Portugal

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target's locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture. Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples' fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hyperbola signatures GPR Classification multi-objective genetic algorithm Mutual information Feature selection Neural networks High order statistics

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Investigation and optimization of polyolefin elastomers polymerization processes using multi- objective genetic algorithm

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2023年第1期193卷 383-393页

作者： Ruan, Shi-Xiang Zhang, Xi-Bao Luo, Zheng-Hong Shanghai Jiao Tong Univ Sch Chem & Chem Engn Dept Chem Engn State Key Lab Met Matrix Composites Shanghai 200240 Peoples R China

The aim of this research is to develop continuous reactor network models to determine the reactor configuration and corresponding optimal operating conditions of a polyolefin elastomers polymerization process. To achieve this objective, a modified vapor-liquid equilibrium model is integrated with both the continuous stirred tank reactor (CSTR) model and the plug flow reactor (PFR) model to accurately predict the vaporization rates of liquid. The multi-objective problem is formulated to maximize the monomer conversion and minimize the total annualized cost simultaneously. To solve the multi-objective problem, the genetic algorithm is utilized to identify the optimal operating conditions. Furthermore, the impact of the reactor combination mode is analyzed by varying the number and sequence of CSTR or PFR. The research findings reveal that a multi-phase CSTR without circulating stream followed by a PFR is the optimal reactor configuration. The proposed configuration can enhance reactor productivity and reduce the total an-nualized cost simultaneously while meeting the desired polymer properties.(c) 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization multi-objective genetic algorithm Polyolefin elastomers polymerization Reactor configuration Operating conditions

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