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14th International Conference on genetic and Evolutionary Computation Conference (GECCO)

作者： Choi, Jaeun Yoon, Yourim Moon, Byung-Ro Seoul Natl Univ Sch Comp Sci & Engn Seoul 151744 South Korea

ISBN: (纸本)9781450311779

In this paper we propose a multi-objective genetic algorithm for the subgraph isomorphism problem. Usually, the number of different edges between two graphs has been used as a fitness function. This approach has limitations in that it only considers directly-visible characteristics of current solutions, not considering the potential for being an optimal solution. We designed a fitness function in which solutions with higher potential can be rated high. This new fitness function has good properties such as transforming the solution space globally convex and improving the performance of local heuristics and genetic algorithms. Experimental results show that the suggested approach brings a considerable improvement in performance and efficiency.

关键词： genetic algorithm subgraph isomorphism fitness function multi-objective genetic algorithm

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Bi-objective Community Detection (BOCD) in Networks Using genetic algorithm

Bi-Objective Community Detection (BOCD) in Networks Using Ge...

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4th International Conference on Contemporary Computing

作者： Agrawal, Rohan Jaypee Inst Informat Technol Dept Comp Sci Noida 201307 Uttar Pradesh India

ISBN: (纸本)9783642226052

A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-objective genetic algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.

关键词： Community Structure Community detection genetic algorithm multi-objective genetic algorithm multi-objective optimization modularity Normalized Mutual Information Bi-objective genetic algorithm

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A multiobjective genetic algorithm for Analysis, Design and Optimization of Antipodal Vivaldi Antennas 9

A Multiobjective Genetic Algorithm for Analysis, Design and ...

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IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC)

作者： Antonio Pumallica-Paro, Marco Luis Arizaca-Cusicuna, Jorge Clemente-Arenas, Mark Univ Nacl San Antonio Abad Cusco Escuela Profes Ingn Elect Cuzco Peru Univ Nacl Ingn INICTEL UNI Inst Nacl Invest & Capacitac Telecomunicac DIDT Grp Radio Frecuencia Microondas & Opt G RFMO Av San Luis 1771 Lima 41 Peru

ISBN: (纸本)9781728105666

In the this contribution, a multi-objective genetic algorithm (MOGA) was implemented to analyze, design and optimize several Antipodal Vivaldi Antennas (AVA) that are being used on Ground Penetration Radars (GPR) applications. This algorithm was implemented and used as an optimization tool in a commercial available software. Directivity, lower cutoff frequency (LCF), bandwidth and cross-polarization were the objectives to be optimized. For the use of the MOGA, the 3D model of the antenna is built in the simulator. Some of its parameters are defined as variables, as the dimensions and shape of the antenna (Individual) depend on the value that its parameters (Genes) take. This work proves to be simple and time saving tool to optimize parameters in a geometry changing system as GPR.

关键词： multi-objective genetic algorithm Antipodal Vivaldi Antennas Ground Penetrating Radar

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Variable Neighborhood multiobjective genetic algorithm for the Optimization of Routes on IP Networks

Variable Neighborhood Multiobjective Genetic Algorithm for t...

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6th International Conference on Evolutionary multi-Criterion Optimization

作者： Onety, Renata E. Moreira, Gladston J. P. Neto, Oriane M. Takahashi, Ricardo H. C. Univ Fed Minas Gerais Sch Elect Engn BR-31270901 Belo Horizonte MG Brazil Univ Fed dos Vales Jequitinhonha & Mucuri Otoni MG Brazil Univ Fed Minas Gerais Dept Math Belo Horizonte MG Brazil

ISBN: (纸本)9783642198922

This paper proposes an algorithm to optimize multiple indices of Quality of Service of multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood multiobjective genetic algorithm (VN-MGA), is a genetic algorithm based on the NSGA-II, with the particular feature that different parts of a solution are encoded differently, at Level 1 and Level 2. In order to improve the results, both representations are needed. At Level 1, the first part of the solution is encoded, by considering as decision variables, the arrows that form the routes to be followed by each request (whilst the second part of the solution is kept constant), whereas at Level 2, the second part of the solution is encoded, by considering as decision variables, the sequence of requests, and first part is kept constant. The preliminary results shown here indicate that the proposed approach is promising, since the Pareto-fronts obtained by VN-MGA dominate the fronts obtained by fixed-neighborhood encoding schemes. Besides the potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms.

关键词： Routing on IP Networks Variable Neighborhood Search multi-objective genetic algorithm

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A Robust Image Watermarking Method in Wavelet Domain Using genetic algorithm

A Robust Image Watermarking Method in Wavelet Domain Using G...

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4th International Conference on Availability, Reliability and Security

作者： Amiri, S. Hamid Jamzad, Mansour Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424435722

Robustness against attacks is an important requirement in image watermarking. This paper presents a robust watermarking algorithm in wavelet transform domain. Firstly, original image is decomposed into its subbands using three level wavelet transform, then, significant coefficients with the same position in HL, LH and HH subbands of the last level are extracted to make a triplet. To embed a watermark bit into a triplet, the standard deviation of triplet coefficients magnitude is set to zero for zero bit or increased for a one bit. Three constant are used to increase the standard deviations of a triplet coefficients. The value of these constants affects the robustness of algorithm and the quality of image. To specify the optimal values for constants a multi-objective genetic algorithm (MOGA) is used. Experimental results reveal the high robustness of the proposed algorithm against common image processing attacks (blurring, median filter, sharpening, noise addition, ect), and print and scan distortion.

关键词： Attacks Fidelity multi-objective genetic algorithm Print and Scan Distortions Robustness Watermarking Wavelet Transform

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genetic algorithm Optimized Grid-based RF Fingerprint Positioning in Heterogeneous Small Cell Networks

Genetic Algorithm Optimized Grid-based RF Fingerprint Positi...

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International Conference on Location and GNSS (ICL-GNSS)

作者： Mondal, Riaz Uddin Ristaniemi, Tapani Turkka, Jussi Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland Magister Solut Ltd Tampere Finland

ISBN: (纸本)9781479998586

In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used multi-objective genetic algorithm (MOGA) which enables autonomous calibration of grid-cell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional grid-based RF fingerprinting.

关键词： Grid-based RF fingerprinting Minimization of Drive Tests multi-objective genetic algorithm Kullback-Leibler Divergence

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multi-Strategy genetic algorithm for Self-configuring Solving of Complex Optimization Problems 4

Multi-Strategy Genetic Algorithm for Self-configuring Solvin...

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IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Sopov, Evgenii Siberian State Aerosp Univ SibSAU Syst Anal & Operat Res Dept Krasnoyarsk Russia

ISBN: (纸本)9781479999583

Many complex optimization problems require a modification of the general evolutionary algorithm (EA) according to the given features of the problem. There exist a great variety of EAs that represent different search strategies for many classes and subclasses of optimization problems. Real-world problems may combine several features that are not known beforehand, thus there is no information about what EA to choose and what EA's settings to apply for efficient problem solving. This study presents a novel metaheuristic for designing multi-strategy EA based on the hybrid of the island model, cooperative and competitive coevolution schemes. The approach controls interactions of EAs and leads to the self-configuring solving of problems with a priori unknown structure. Two examples of implementations of the approach for multi-objective and non-stationary optimization are discussed. The results of numerical experiments for benchmark problems from CEC competitions are presented. The proposed approach has demonstrated the efficiency comparable with other well-studied techniques for multi-objective and non-stationary optimization. And it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.

关键词： self-configuration multi-strategy evolutionary algorithm multi-objective genetic algorithm non-stationary optimization

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genetic algorithm-based Optimization of ELM for On-line Hyperspectral Image Classification

Genetic Algorithm-based Optimization of ELM for On-line Hype...

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International Joint Conference on Neural Networks (IJCNN)

作者： Echanobe, J. del Campo, I. Martinez, Victoria Basterretxea, K. Univ Basque Country UPV EHU Dept Elect & Elect Leioa Basque Country Spain Univ Basque Country UPV EHU Dept Elect Technol Bilbao Basque Country Spain

ISBN: (纸本)9781509061822

Hyperspectral remote sensing is becoming an active research field in the last decades thanks to the availability of efficient machine learning algorithms and also to the ever-increasing computation power. However, there exist application domains (e.g., embedded applications) in which the deployment of this kind of systems becomes unfeasible due to the high requirements related to the size, power consumption or processing speed. A way to overcome this trouble consists on using any method able to scale-down the dimensionality of the problem and/or to reduce the complexity of the machine learning models. In this paper, we propose the use of a multiobjective genetic algorithm to minimize both the dimension of the input space and the size of the machine learning model. In particular, we have developed a hyperspectral image classifier based on an Extreme Learning Machine (ELM) for which the number of system inputs (dimensionality) and the number of hidden neurons are minimized without decreasing its performance. The system is evaluated by using a known benchmark dataset.

关键词： Extreme Learning Machines (ELM) multi-objective genetic algorithm Machine Learning Hyperspectral remote sensing Dimensionality reduction

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Development and simulation of a gridded thermionic cathode electron gun for a high-energy photon source

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Nuclear Science and Techniques 2023年第3期34卷 73-81页

作者： Sheng-Chang Wang Da-Yong He Cai Meng Jing-Yi Li Zu-Sheng Zhou Jin-Dong Liu Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China

A gridded thermionic cathode electron gun was developed for the linear accelerator of the High Energy Photon Source(HEPS).An electron gun should provide a large maximum bunch charge with a wide adjustable *** satisfy these requirements,the shape of the electrode was optimized using a multi-objective genetic algorithm.A large bunch charge with an adjustable range was achieved using the grid-limited gun,the flow of which was analyzed using 3-D *** electron gun has been manufactured and tested,and the measured data of the grid-limited current and simulation results are compared and discussed in this study.

关键词： Electron gun multi-objective genetic algorithm Grid CST

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multi-objective design optimization of composite submerged cylindrical pressure hull for minimum buoyancy and maximum buckling load capacity

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Defence Technology（防务技术） 2021年第4期17卷 1190-1206页

作者： Muhammad Imran Dong-yan Shi Li-li Tong Ahsan Elahi Hafiz Muhammad Waqas Muqeem Uddin College of Mechanical and Electrical Engineering Harbin Engineering University Harbin 150001China College of Aerospace and Civil Engineering Harbin Engineering University Harbin 150001China Pakhtunkhwa Energy Development Organization(PEDO) Khyber PakhtunkhwaPakistan

This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic *** design optimization study is conducted for cross-ply layups[0_(s)/90_(t)/0_(u)],[0_(s)/90_(t)/0_(u)]s,[0_(s)/90_(t)]s and[90_(s)/0_(t)]s considering three uni-directional composites,***/Epoxy,Glass/Epoxy,and Boron/*** optimization study is performed by coupling a multi-objective genetic algorithm(MOGA)and Analytical *** the buoyancy factor and maximizing the buckling load factor are considered as the objectives of the optimization *** objectives of the optimization are achieved under constraints on the Tsai-Wu,Tsai-Hill and Maximum Stress composite failure criteria and on buckling load *** verify the optimization approach,optimization of one particular layup configuration is also conducted in ANSYS with the same objectives and constraints.

关键词： multi-objective genetic algorithm Optimization Composite submersible pressure hull Thin shell Material failure Shell buckling

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