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A control method for autonomous mobility management systems toward 5G mobile networks

A control method for autonomous mobility management systems ...

IEEE International Conference on Communications (ICC) / Wireless Communications Symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Kominami, Daichi Iwai, Takanori Shimonishi, Hideyuki Murata, Masayuki Osaka Univ Grad Sch Econ Suita Osaka Japan Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan NEC Corp Ltd Syst Platform Res Labs Tokyo Japan

ISBN: (纸本)9781509015252

Fifth-generation mobile and wireless communication systems are actively being studied as a next-generation mobile communication network. People and devices will connect to the Internet via wireless networks in future communication networks, but many challenges must be solved to realize 5G networks. For example, it is difficult to manage connected user devices using centralized control in the control plane. We previously proposed an architecture for autonomous and distributed mobility management and a biology-inspired mobility management scheme that adaptively selects the location of management entities. However, this scheme has some problems from a network-wide perspective, due to entities' autonomous decision-making. This paper introduces a control node for monitoring and managing the network to resolve the otherwise unsolvable problem of autonomous distributed control. We show that this control node can improve network stability without much loss of performance in the entire network.

关键词： 5G network attractor selection bio-inspired algorithm controlled self-organization

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A Modified Ant Colony Optimization algorithm for the Distributed Job shop Scheduling Problem 21

A Modified Ant Colony Optimization algorithm for the Distrib...

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21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems (KES)

作者： Chaouch, Iman Driss, Olfa Belkahla Ghedira, Khaled Univ Manouba COSMOS Lab Tunis Tunisia Univ Manouba Ecole Natl Sci Informat Tunis Tunisia Univ Manouba Ecole Super Commerce Tunis Tunisia Univ Tunis Inst Super Gest Tunis Tunis Tunisia

The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. The objective is to minimize the global makespan over all the factories. This paper is a first step to deal with the DJSP using three versions of a bio-inspired algorithm, namely the Ant Colony Optimization (ACO) which are the Ant System (AS), the Ant Colony System (ACS) and a Modified Ant Colony Optimization (MACO) aiming to explore more search space and thus guarantee better resolution of the problem. Comprehensive experiments are conducted to evaluate the performance of the three algorithms and the results show that the MACO is effective for the problem and AS and ACS algorithms in resolving the DJSP. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： bio-inspired algorithm Job shop Makespan Multi-factory Scheduling Swarm Intelligence

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Improved Differential Evolution algorithm

Improved Differential Evolution Algorithm

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International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)

作者： Jain, Sanjay Kumar, Sandeep Sharma, Vivek Kumar Sharma, Harish Amity Univ Jaipur Jaipur Rajasthan India Jagannath Univ Jaipur Rajasthan India Rajasthan Tech Univ Kota India

ISBN: (纸本)9781538605141

Differential Evolution ( DE) is an evolutionary approach to unravel complex optimization problems. The DE is a straight forward and very popular population based stochastic algorithm. DE outperformed other competitive evolutionary algorithms when measured over benchmark problem as well as actual optimization problems in terms of performance. The major drawback of DE is early convergence and stagnation at sub-optimal points, like other stochastic optimization algorithms. So as to overcome these problems this paper presents hybrid of DE with levy flight search strategy. The anticipated algorithm tested over a set of benchmark problems to demonstrate its superiority over other popular modification of DE.

关键词： Evolutionary algorithm bio-inspired algorithm Levy Flight Search Computational Intelligence

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Implementation of Chicken Swarm Optimization(CSO) with Partial Transmit Sequences for the Reduction of PAPR in OFDM System 9

Implementation of Chicken Swarm Optimization(CSO) with Parti...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： A H M Rafiul Awal Zheng Dou Md Al Shayokh Muhammad Irshad Zahoor College of Information and Communication Engineering Harbin Engineering University

ISBN: (纸本)9781509038237;9781509038220

Computational complexity considers as challenging issue in conventional Partial Transmit Sequence(PTS) scheme, even though it has good performance to reduce PAPR. In this paper, one of the recently developed bio-inspired algorithm named Chicken Swarm Optimization(CSO) is implemented with the combination of Partial Transmit Sequences to reduce PAPR, one of the most featured problems in OFDM system. Results show that CSO with PTS scheme reduces the computational complexity and improves the reduction of PAPR up to 2.25 d B at 0.0011 complementary cumulative distribution function(CCDF) for an unmodified OFDM signal.

关键词： bio-inspired algorithm CSO partial transmit sequence peak to average power ratio computational complexity

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bio-inspired algorithms Applied in Multi-objective Vehicle Routing Problem: Frameworks and Applications 10th

Bio-inspired Algorithms Applied in Multi-objective Vehicle R...

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10th International Conference on bio-inspired Computing - Theories and Applications (BIC-TA)

作者： Wang, Yuan He, Yongming He, Lei Xing, Lining Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783662490143;9783662490136

Multi-objective vehicle routing problem (MOVRP) is developed from vehicle routing problem (VRP). MOVRP is a classic multi-objective optimization problem. During the recent years, the MOVRPs had a progress in problem scales and complex level. As a result, to get better solutions of MOVRPs, bio-inspired algorithms were introduced into this area. This article first analyses the MOVRP framework, and then reviews the bio-inspired algorithm frameworks that designed to solve MOVRPs. This analysis leads to the identification of bio-inspired algorithms which can get better solutions for MOVPRs and can be applied to real-life cases successfully.

关键词： bio-inspired algorithm Multi-objective Vehicle routing Literature review

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Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

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Journal of Communications and Information Networks 2017年第4期2卷 107-119页

作者： Veeran Ranganathan Balasaraswathi Muthukumarasamy Sugumaran Yasir Hamid Department of Computer Science Engineering Pondicherry Engineering CollegePondicherry UniversityPondicherry 605014India

As Internet access widens,IDS(Intrusion Detection System)is becoming a very important component of network security to prevent unauthorized use and misuse of *** IDS routinely handles massive amounts of data traffic that contain redundant and irrelevant features,which impact the performance of the IDS *** selection methods play an important role in eliminating unrelated and redundant features in *** analysis,neural networks,machine learning,data mining techniques,and support vector machine models are employed in some such *** feature selection leads to better classification ***,bio-inspired optimization algorithms have been used for feature *** work provides a survey of feature selection techniques for IDS,including bio-inspired algorithms.

关键词： IDS feature selection optimization classification bio-inspired algorithm

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Stochastic Navigation in Smart Cities

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ENERGIES 2017年第7期10卷 929-929页

作者： Martin Garcia, Ruben Prieto-Castrillo, Francisco Villarrubia Gonzalez, Gabriel Prieto Tejedor, Javier Manuel Corchado, Juan Univ Salamanca BISITE Res Grp Edificio I D i E-37008 Salamanca Spain MIT Media Lab Cambridge MA 02139 USA Harvard Univ Harvard TH Chan Sch Publ Hlth Boston MA 02115 USA StageMotion R&D Dept C Orfebres 10 Palencia 34005 Spain

In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end we have adapted the classical ant foraging swarm algorithm to urban morphologies. A perturbed Markov chain model is shown to qualitatively reproduce the observed behaviour. This consists of perturbing the lattice random walk with a set of perturbing sources. As the number of sources increases the exploration times decrease consistently with the swarm algorithm. This model provides a better understanding of underlying process dynamics. An experimental campaign with real prototypes provided experimental validation of our models. This enables us to extrapolate conclusions to optimize electric vehicle routing in real city topologies.

关键词： electric vehicle routing charging stations bio-inspired algorithm stochastic process smart cities

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A Modified Ant Colony Optimization algorithm for the Distributed Job shop Scheduling Problem

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Procedia Computer Science 2017年 112卷 296-305页

作者： Imen Chaouch Olfa Belkahla Driss Khaled Ghedira COSMOS Laboratory Université de la Manouba Tunisie Ecole Nationale des Sciences de l’Informatique Université de La Manouba Tunisia Ecole Supérieure de Commerce Université de La Manouba Tunisia Institut Supérieur de Gestion de Tunis Université de Tunis Tunisia

关键词： bio-inspired algorithm Job shop Makespan Multi-factory Scheduling Swarm Intelligence

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An orthogonal-design hybrid particle swarm optimiser with application to capacitated facility location problem

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2016年第5期8卷 268-285页

作者： Chu, Xianghua Niu, Ben Liang, J. J. Lu, Qiang Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Guangdong Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Guangdong Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China

To improve the performance of particle swarm optimiser (PSO) for global optimisation, a variant called orthogonal-design hybrid particle swarm optimiser (OHPSO) is presented in this paper. A permutation strategy based on orthogonal experimental design is developed as a metabolic mechanism to enhance population diversity. In addition, a hybrid learning strategy is proposed to exploit the particles' best experiences and direct the individuals more efficiently. OHPSO is tested on a set of 18 benchmark functions with various properties, and nine state-of-the-art PSO variants are adopted for comparison. Experimental results and statistical analyses indicate a significant improvement of the proposed algorithm. Furthermore, OHPSO is applied to a practical engineering problem, the capacitated facility location problem, to justify its real-world performance and applicability. The experiment results are highly competitive with existing bio-inspired algorithms in the location optimisation.

关键词： particle swarm optimiser PSO global optimisation orthogonal experimental design hybrid learning capacitated facility location problem population diversity bio-inspired algorithm

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Improved cervix lesion classification using multi-objective binary firefly algorithm-based feature selection

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2016年第6期8卷 367-378页

作者： Sahoo, Anita Chandra, Satish JSS Acad Tech Educ Dept Comp Sci & Engn Noida 201301 India Jaypee Inst Informat Technol Dept Comp Sci Noida 201301 India

Cervical cancer is one of the vital and most frequent cancers, but can be cured if correctly diagnosed. This work is a novel effort towards developing a methodology for effective characterisation of cervix lesions that may assist radiologists in the diagnostic process by providing a reliable and objective discrimination of benign and malignant lesions in contrast enhanced CT-Scan images. Feature selection, which is a key stage in building such efficient classification models, is NP-hard;where, randomised algorithms do better. Since, firefly algorithm is an efficient biologically inspired randomised algorithm;here it has been utilised for optimal feature selection. This paper presents a multi-objective binary firefly algorithm for wrapper-based feature selection and utilises the selected feature subset for improved classification of cervix lesions. For experiments, contrast enhanced CT-Scan images of 22 patients have been used, where all lesions had been recommended for surgical biopsy by specialists. For characterisation of lesions, grey-level cooccurrence matrix-based texture features are extracted from two-level decomposition of wavelet coefficients. The objective function is designed to minimise the classification error and feature subset length both;making it multi-objective. With 94% accuracy in lesion classification, it has superior performance and greatly reduced execution time than multi-objective genetic algorithm-based feature selection.

关键词： cervix lesion characterisation classification feature selection firefly algorithm multi-objective optimisation textural features wavelet features wrapper-based feature selection bio-inspired algorithm

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