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An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 146卷 109-123页

作者： Hajimani, Elmira Ruano, M. G. Ruano, A. E. Univ Algarve Fac Sci & Technol Faro Portugal Univ Coimbra CISUC Coimbra Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal

objective: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. Methods: For the design of a neural network classifier, a multi objective genetic algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Results: Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602 pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). (C) 2017 Published by Elsevier Ireland Ltd.

关键词： Neural networks Symmetry features multi-objective genetic algorithm Intelligent support systems Cerebral vascular Accident

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Uncertain multi-objective optimal model of oilfield development planning and its algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2017年第5期8卷 769-779页

作者： Ji, Xiaoyu Yan, Sen Feng, Siyu Renmin Univ China Sch Business Beijing 100872 Peoples R China

In this paper, we discuss the formulation of the oilfield development plan in case of significant nondeterminacy in oilfield development. And the plan needs to ensure production and achieve minimum cost and maximum new recoverable reserves. The uncertain factors in oilfield development are analyzed in this paper, and we consider the uncertain nature of the stimulation effect of measures and new recoverable reserves per well and quantify them. On this basis, an uncertain multi-objective optimal model of oilfield development planning is constructed. The model aims to minimize the expectation of development cost and maximize the expectation of new recoverable reserves, and optimizes the workload of each measure under the constraints including the oil production and the resources limitation. Based on uncertainty theory, the model is transformed into a deterministic model. And a nondominated sorting genetic algorithm with elite strategy is developed to solve the model and get the Pareto solution set. Then the multi-attribute decision-making is applied to select the multiple development plans, which provides the basis for the decision-making of the oilfield development plan. Finally, a numerical example is given to verify the effectiveness of the model and algorithm, and their practical application values under the background of oilfield development planning.

关键词： Oilfield development planning Uncertain programming Uncertain multi-objective programming multi-objective genetic algorithm

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MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

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INFORMATION 2018年第9期9卷 233-233页

作者： Nascimento, Zuleika Sadok, Djamel Univ Fed Pernambuco UFPE Informat Ctr BR-50740560 Recife PE Brazil

Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users' privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a multi-objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods.

关键词： network traffic classification machine learning hybrid model multi-objective genetic algorithm extreme learning machine growing hierarchical self-organizing map

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multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 89卷 80-87页

作者： Zheng, Jia-Nian Chien, Chen-Fu Gen, Mitsuo Natl Tsing Hua Univ Hsinchu 30013 Taiwan Fuzzy Log Syst Inst Iizuka Fukuoka 8200067 Japan

The container loading problem (CLP) has important industrial and commercial application for global logistics and supply chain. Many algorithms have been proposed for solving the 2D/3D container loading problem, yet most of them consider single objective optimization. In practice, container loading involves optimizing a number of objectives. This study aims to develop a multi-objective multi-population biased random-key genetic algorithm for the three-dimensional single container loading problem. In particular, the proposed genetic algorithm applied multi-population strategy and fuzzy logic controller (PLC) to improve efficiency and effectiveness. Indeed, the proposed approach maximizes the container space utilization and the value of total loaded boxes by employing Pareto approach and adaptive weights approach. Numerical experiments are designed to compare the results between the proposed approach and existing approaches in hard and weak heterogeneous cases to estimate the validity of this approach. The results have shown practical viability of this approach. This study concludes with discussions of contributions and future research directions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Container loading genetic algorithm multi-objective genetic algorithm Fuzzy logic controller Cutting and packing

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Joint Optimal Channel Allocation, Interface Assignment and Routing in multi-hop Wireless Networks 20

Joint Optimal Channel Allocation, Interface Assignment and R...

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20th International Conference on Advanced Communication Technology (ICACT)

作者： Wu, Jie Li, Hongchun Xu, Yi Tian, Jun Fujitsu Res & Dev Ctr Co Ltd Beijing Peoples R China

ISBN: (纸本)9791188428014

multi-hop wireless networks have advantages over the single-hop ones in terms of reliability and coverage range. Moreover, the capacity of multi-hop wireless network can be substantially increased via multiple radios tuned to non-overlapping channels. However, the channel allocation, network interface cards assignment and routing remain challenging due to the interference of the neighboring transmissions. These three problems have proved to be a NP-hard problem. Previous studies separating the routing from the channel allocation, instead of considering the three problems as a whole, cannot get the overall optimal solution. In this work, we employ an improved multi-objective genetic algorithm to optimize the channel allocation, interface assignment and the routing, so as to minimize the overall network interference. The proposed algorithm includes two parts: 1) dynamic genetic mutation based on diversity measure;and 2) elite preservation based on ideal points. In order to eliminate illegal solutions, a new individual encoding method is proposed. In addition, an interference model taking into account the effects of channel separation and the traffic of neighbor links is applied to evaluate the quality of the interference of the network. Finally, a fitness function is defined to obtain the best search results. Simulation results show that our improved multi-objective genetic algorithm can reduce the interference and cost of total network compared to the standard genetic algorithm.

关键词： multi-objective genetic algorithm channel allocation interface assignment routing multi-hop wireless network

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A NSGA-Ⅱ based Peak Load Shifting Optimization Method for Customers with Distributed Generators Considering Time-of-use Price

A NSGA-Ⅱ based Peak Load Shifting Optimization Method for C...

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第37届中国控制会议

作者： Hao Zhou Min Ding Bo Hu Qingyi Wang Zhili Tan Danyun li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System State Grid Energy Research Institute Co. LTD.

With the extensive use of distributed generation, the traditional demand response analysis cannot meet the current *** paper proposes a NSGA-II based peak load shifting optimization method for customers with distributed generators considering time-of-use price. Firstly, a fuzzy classification method divides the daily power into three time segments,peak hours, flat hours and valley hours. Secondly, on the basis of the time-of-use price, a peak load shifting optimization model is built with constraints and the objectives of minimizing the peak load, maximizing the valley load, and minimizing the peakvalley difference. Then, a NSGA-II based optimization method solves the optimal model and obtains the optimal electricity prices of different time segments to shift the peak load and the valley load. Finally, the simulation results shows the effectiveness of the proposed method.

关键词： Distributed Generation Demand Respond Time-of-use Price multi-objective genetic algorithm

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multi-satellite Observation Scheduling for Large Area Disaster Emergency Response

Multi-satellite Observation Scheduling for Large Area Disast...

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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： X.N.Niu H.Tang L.X.Wu Key Laboratory of Environment Change and Natural Disaster Ministry of EducationBeijing Normal University Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities Beijing Normal University School of Geosciences and Info-Physics Central South University

Timely acquiring remote sensing data is very important for rapid response to disasters. Satellite task scheduling aiming at making an optimal imaging plan, plays a key role in coordinating multiple satellites to monitor the disaster area. In the paper, to generate imaging plan dynamically according to the disaster relief, we propose a dynamic satellite task scheduling method for large area disaster response. First, an initial robust scheduling scheme is generated by a robust satellite scheduling model in which both the profit and the robustness of the schedule are simultaneously maximized. Then, we use a multi-objective optimization model to obtain a series of decomposing schemes. Based on the initial imaging plan, we propose a mixed optimizing algorithm named HASGA-II to allocate the decomposing results thus to obtain an adjusted imaging schedule. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of rapid response using satellite resources and used to evaluate the performance of the proposed method with state-of-the-art approaches. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

关键词： Disaster Emergency Response Area Tasks Decomposition multi-satellite Scheduling multi-objective genetic algorithm

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Handling swarm of UAVs based on evolutionary multi-objective optimization

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2017年第3期6卷 263-274页

作者： Ramirez-Atencia, Cristian R-Moreno, Maria D. Camacho, David Univ Autonoma Madrid C Tomas & Valiente 11 E-28049 Madrid Spain Univ Alcala De Henares Carretera Madrid BarcelonaKm 33 600 Madrid 28871 Spain

The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.

关键词： Unmanned aerial vehicles Mission planning Constraint satisfaction problems multi-objective genetic algorithm

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Optimal position of rectangular vortex generator for heat transfer enhancement in a flat-plate channel

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ASME Summer Heat Transfer Conference

作者： Khan, Tariq Amin Li, Wei Zhang, Zhengjiang Du, Jincai Khan, Sadiq Amin Liu, Fu Zhejiang Univ Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Collaborat Innovat Ctr Adv Aeroengine Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Zhejiang Univ Dept Mat Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9780791857885

Heat transfer is a naturally occurring phenomenon which can be greatly enhanced by introducing longitudinal vortex generators (VGs). As the longitudinal vortices can potentially enhance heat transfer with small pressure loss penalty, VGs are widely used to enhance the heat transfer of flat-plate type heat exchangers. However, there are few researches which deal with its thermal optimization. Three dimensional numerical simulations are performed to study the effect of angle of attack and attach angle (angle between VG and wall) of vortex generator on the fluid flow and heat transfer characteristics of a flat-plate channel. The flow is assumed as steady state, incompressible and laminar within the range of studied Reynolds numbers (Re = 380, 760, 1140). In the present work, the average and local Nusselt number and pressure drop are investigated for Rectangular vortex generator (RVG) with varying angle of attack and attach angle. The numerical results indicate that the heat transfer and pressure drop increases with increasing the angle of attack to a certain range and then decreases with increasing angle of attack. Moreover, the attach angle also plays an importance role;a 90 attach angle is not necessary for enhancing the heat transfer. Usually, heat transfer enhancement is achieved at the expense of pressure drop penalty. To find the optimal position of vortex generator to obtain maximum heat transfer and minimum pressure drop, the data obtained from numerical simulations are used to train a BRANN (Bayesian-regularized artificial neural network). This in turn is used to drive multi-objective genetic algorithm (MOGA) to find the optimal parameters of VGs in the form of Pareto front. The optimal values of these parameters are finally presented.

关键词： Heat transfer enhancement longitudinal vortex generator flat-plate channel Artificial neural network multi-objective genetic algorithm

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Optimal Grid Connected Hybrid Energy System for Egyptian Residential Area

Optimal Grid Connected Hybrid Energy System for Egyptian Res...

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International Conference on Sustainable Energy Engineering and Application (ICSEEA) - Continuous Improvement of Sustainable Energy for Eco-Mobility

作者： Abdelshafy, Alaaeldin M. Hassan, Hamdy Mohamed, Abdelfatah M. El-Saady, G. Ookawara, Shinichi E JUST Alexandria Egypt Assiut Univ Fac Engn Assiut Egypt Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781538617656

This article presents a grid-connected hybrid energy system (HES) including a photovoltaic array (PV), wind turbines (WT), fuel cell (FC), electrolyzer, diesel generator, hydrogen tanks (HT) and battery systems. A multi-objective genetic algorithm (MOGA) is applied to find the optimal size of HES components with zero load rejection and selling the excess energy to the grid. The main aim of this article is to minimize the cost of electricity generated (COE) and CO2 emission simultaneously. A case study proposed hybrid energy system loads are some residential buildings (one block) in New Assiut City, Egypt with a peak value of 160.2 kW and yearly energy of 430 MWh. An optimal planning is obtained with a lifetime of 20 years. A comparison between different configurations of HES is done. Results showed that HES comprising PV/WT/battery/diesel is the most feasible economically and produce less CO2 emission than other configurations. Further, it is helping to decrease the load demand of the utility grid.

关键词： Photovoltaic array Wind turbine Battery system fuel cell electrolyzer multi-objective genetic algorithm

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