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international workshop on Reliable Networks Design and Modeling (RNDM)

作者： Krzysztof Walkowiak Michał Kucharzak Paweł Kopeć Andrzej Kasprzak Department of Systems and Computer Networks Wroclaw University of Technology Poland

Elastic Optical Network (EON) is a promising technology proposed recently to provide flexible and multi-bitrate data transmission in the optical layer. EONs utilize various modulation format conversion and smaller spectrum granularity, what provide more efficient use of spectrum resources comparing to currently used fix-grid WDM networks. Concurrently, growing popularity of network services provided by Data Centers located in different network nodes and related to cloud computing and Content Delivery Network applications triggers the need to address anycast transmissions as an effective way to improve network performance. This paper focuses on modeling and optimization of anycast flows in EONs in the context of network restoration. Specifically, we formulate an original NP-hard Routing and Spectrum Allocation for Restoration of Anycast Flows (RSA/RAF) problem. In order to solve the problem, we design, implement and evaluate four greedy algorithms and a metaheuristic derived from Simulated Annealing. Extensive numerical experiments prove high practical applicability of presented algorithms.

关键词： Heuristic algorithms optimization Tuning Modulation Resource management Optical fiber networks Routing

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Optimal multilevel thresholding using the modified adaptive particle swarm optimization

International Journal of Digital Content Technology and its ...

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international Journal of Digital Content Technology and its applications 2012年第15期6卷 208-219页

作者： Liu, Yi Mu, Caihong Kou, Weidong School of Electronic Engineering Xidian University Xi'an China School of Telecommunications Engineering Xidian University Xi'an China IBM Beijing China

The conventional multilevel thresholding methods are computational expensive since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, the modified adaptive particle swarm optimization (MAPSO) algorithm is proposed to overcome this drawback. The dynamic population (DP) strategy of the proposed algorithm enables the population size variable with the evolutionary state at run time. With the help of DP strategy, the population size can increase when the swarm converges and decrease when the swarm disperses. The MAPSO algorithm is used to find the optimal thresholds by maximizing the Otsu's objective function. The performance of the proposed algorithm has been validated on eight standard test images. The experimental results of 50 independent runs illustrate the best solution quality and stability of the MAPSO when compared with three other PSO algorithms.

关键词： Image segmentation

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Comparative Study of Different Salp Swarm Algorithm Improvements for Feature Selection applications

Comparative Study of Different Salp Swarm Algorithm Improvem...

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Impedance Spectroscopy (IWIS), international workshop on

作者： Ayoub Choura Hiba Hellara Mouna Baklouti Olfa Kanoun National Engineering School of Sfax Sfax Tunisia University of Technology of Chemnitz Chemnitz Germany

Recently, many optimization algorithms have been applied for Feature Selection (FS) problems and still showing very promising results. Moreover, Salp Swarm Algorithm (SSA) is one of the most sophisticated meta-heuristic swarm-based optimization algorithms. As SSA is proving its efficiency, it has undergone several improvements in order to make its performance as better as it could be. In this context, this paper presents a comparative study of the Salp Swarm Algorithm variants for feature selection application. These different improvement approaches aim to reduce the number of features and eliminate the non-useful ones. This study focuses on three binary versions of SSA, namely Binary Salp Swarm Algorithm (BSSA), Chaotic Salp Swarm Algorithm (CSSA) and Dynamic Salp Swarm Algorithm (DSSA). For this purpose, 13 UCI benchmark datasets were used. Based on the comparative results we can conclude that the DSSA approach enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature.

关键词： Spectroscopy Heuristic algorithms Conferences Sociology Benchmark testing Feature extraction Impedance

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Using genetic algorithms for robust optimization in financial applications

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Neural Network World 1995年第4期5卷 573-587页

作者： Pictet, O.V. Dacorogna, M.M. Chopard, B. Oussaidene, M. Schirru, R. Tomassini, M. Olsen & Associates Zurich Switzerland

In this study, optimal indicators and strategies for foreign exchange trading models are investigated in the framework of genetic algorithms. We first explain how the relevant quantities of our application can be encoded in `genes' so as to fit the requirements of the genetic evolutionary optimization technique. In financial problems, sharp peaks of high fitness are usually not representative of a general solution but, rather, indicative of some accidental fluctuations. Such fluctuations may arise out of inherent noise in the time series or due to threshold effects in the trading model performance. Peaks in such a discontinuous, noisy and multimodal fitness space generally correspond to trading models which will not perform well in out-of-sample tests. In this paper we show that standard genetic algorithms will be quickly attracted to one of the accidental peaks of the fitness space whereas genetic algorithm for multimodal functions employing clustering and a specially designed fitness sharing scheme will find optimal parameters which correspond to broad regions where the fitness function is higher on average. The optimization and the quality tests have been performed over eight years of high frequency data of the main foreign exchange rates.

关键词： Financial data processing

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optimization of nonlinear programming problems using non-traditional method: genetic algorithms 10

Optimization of nonlinear programming problems using non-tra...

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Proceedings of the international Conference and workshop on Emerging Trends in Technology

作者： P. S. Revankar Smita L. Kasar Abhilasha Mishra

ISBN: (纸本)9781605588124

The application of Genetic algorithms to optimize the Nonlinear Programming Problems is presented. The applications of nonlinear programming problems ranges from data networks-routing, production planning, resource allocation, computer aided design etc. The paper focuses on quadratic programming problems traditionally solved by using Beale's method. The paper presents a method of global optimization based on genetic algorithms. The Genetic algorithms are a versatile tool, which can be applied as a global optimization method to problems of engineering. The calculated values are compared with the given constraints of problem. The results are in very good agreement with decreased computational time reported elsewhere.

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Sightseeing route planning problem by electric vehicle on the Time-Expanded Network

Sightseeing route planning problem by electric vehicle on th...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Takashi Hasuike Hiroe Tsubaki Hideki Katagiri Hiroshi Tsuda Graduate School of Information Science and Technology Osaka University Japan Suita Osaka Japan Department of Data Science Institute of Statistical Mathematics Japan Tachikawa Tokyo Japan Graduate School of Engineering Hiroshima University Japan Higashi-Hiroshima Hiroshima Japan Faculty of Science and Engineering Doshisha University Japan Kyotanabe Kyoto Japan

This paper proposes a sightseeing route planning problem by electric vehicle with time-dependency of traveling times between any two sightseeing sites. In order to represent the time-dependency, Time-Expanded Network (TEN), which contains a copy to the set of nodes in the underlying static network for each discrete time step, is introduced. In addition, it is hard for the tourist to set satisfaction values at sightseeing sites numerically, but it is not difficult to classify sightseeing sites into several groups. Therefore, the objective function of our proposed model is set to maximize the total visiting sightseeing sites in each group. The problem is a multiobjective programming problem, and hence, a principle of compromise is introduced to solve our proposed problem in network optimization. Furthermore, the main constrained network optimization problem is equivalently transformed into a recursive equation, and an exact and greedy algorithm is developed.

关键词： Planning optimization Mathematical model Batteries Indexes Linear programming Heuristic algorithms

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Hardware/Software co-design SoC-system for a neural network trained by particle swarm optimization

Hardware/Software co-design SoC-system for a neural network ...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Yukinobu Hoshino Kochi Koka Daigaku Kami Kochi JP

ISBN: (纸本)9781538604700;9781538604694

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

关键词： Artificial neural networks Intelligent systems Particle swarm optimization Convergence Training Conferences

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optimization of Radar Resource Scheduling Based on Improved Ant Colony Algorithm

Optimization of Radar Resource Scheduling Based on Improved ...

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Z. X. Huang S. C. Hu B. K. Zhang Y. X. Liu S. He W. B. Li Unit 63768 of PLA

In the case of limited radar resources, the resource scheduling strategy is of great significance to improve the space target cataloging ability. The traditional radar resource scheduling strategy uses fixed rules to allocate radar resources for observation requirements, which cannot maximize radar resources in practical *** this paper, a radar resource scheduling optimization method based on improved ant colony algorithm is proposed, which quantifies the space target observation requirements weighted by the task priority, and optimizes the scheduling result with the highest requirement satisfaction, so that the radar resources can be optimally allocated and the ability of space observation be improved.

关键词： Radio frequency Phased arrays Scheduling algorithms Spaceborne radar Conferences optimization methods Microwave theory and techniques

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The Methods and Means of Efficiency Increasing the Linear optimization Problems Solving in Project Management

The Methods and Means of Efficiency Increasing the Linear Op...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Sergii K. Chernov Sergey D. Titov Ludmila S. Chernova Lubava S. Chernova Diana Zahorodnia Taras Lendiuk Admiral Makarov National University of Shipbuilding Mykolaiv Ukraine West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9781665426060

Modern computer combinatorial methods for solving problems linear optimization requires the development of algorithms that allow to obtain an approximate solution with a guaranteed estimate of the value of the objective function. It is important to simplify the mathematical model prepared for the beginning of computer implementation. Such expediency stimulates the development of effective and improvement of existing algorithms for preparation for computer calculations. The use of such algorithms will significantly reduce the computer time of calculations and reduce the hardware requirements for the computer. The presented work is devoted to the construction of a chain of efficient algorithms that simplify the original mathematical model of the problem and the implementation of its computer calculation. The aim of the work is to build effective algorithms and general principles of preparation for computer solution of LO problems with their illustration on different model problems.

关键词： Computational modeling Project management Approximation algorithms Search problems Linear programming Mathematical models Hardware

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A new distributed modified extremal optimization for optimizing protein structure alignment

A new distributed modified extremal optimization for optimiz...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Keiichi Tamura Hajime Kitakami Tatsuhiro Sakai Yoshifumi Takahashi Hiroshima Shiritsu Daigaku Hiroshima JP Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

Identifying similar structures in proteins has emerged as one of the most attractive research topics in the post-genome era. Protein structure alignment, which is similar to sequence alignment, identifies the structural homology between two protein structures according to their three-dimensional conformation. One of the simplest yet most robust techniques for optimizing protein structure alignment is the contact map overlap maximization problem (the CMO problem). In this paper, we focus on heuristics for the CMO problem. In our previous work, we proposed a bio-inspired heuristic using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and the island model. DMEO enhances population diversity; however, individual evolution is extremely monotonous because evolutions of it is based on the greedy moving approach. To address this issue, we propose a novel bio-inspired heuristic, i.e., DMEO with different evolutionary strategy (DMEODES). DMEODES is also based on the island model; however, some of the islands, called hot-spot islands, have a different evolutionary strategy. To evaluate DMEODES, we used actual protein structures. Experimental results showed that DMEODES outperforms DMEO.

关键词： Proteins optimization Sociology Statistics Biological system modeling Linear programming Genetic algorithms

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