• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4228-4247
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• 2018

Many methods have been developed for the vehicles to create clusters in vehicular ad hoc networks (VANETs). Usually, nodes are vehicles in the VANETs, and they are dynamic in nature. Clusters of vehicles are made for making the communication between the network nodes. Cluster Heads (CHs) are selected in each cluster for managing the whole cluster. This CH maintains the communication in the same cluster and with outside the other cluster. The lifetime of the cluster should be longer for increasing the performance of the network. Meanwhile, lesser the CH's in the network also lead to efficient communication in the VANETs. In this paper, a novel algorithm for clustering which is based on the social behavior of Gray Wolf Optimization (GWO) for VANET named as Intelligent Clustering using Gray Wolf Optimization (ICGWO) is proposed. This clustering based algorithm provides the optimized solution for smooth and robust communication in the VANETs. The key parameters of proposed algorithm are grid size, load balance factor (LBF), the speed of the nodes, directions and transmission range. The ICGWO is compared with the well-known meta-heuristics, Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) for clustering in VANETs. Experiments are performed by varying the key parameters of the ICGWO, for measuring the effectiveness of the proposed algorithm. These parameters include grid sizes, transmission ranges, and a number of nodes. The effectiveness of the proposed algorithm is evaluated in terms of optimization of number of cluster with respect to transmission range, grid size and number of nodes. ICGWO selects the 10% of the nodes as CHs where as CLPSO and MOPSO selects the 13% and 14% respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.149-150
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• 2010

• Journal of Power Electronics
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• v.3 no.2
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• pp.69-80
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• 2003

Computer aided engineering (CAE) is a systematic approach to develop a better product/application with maximum possible options and minimum transition time. This paper presents a comprehensive feasibility analysis of various CAE techniques for evaluation and optimization of power electronic converters and systems. Different CAE methods for analysis, design, and performance improvement are classified. In addition, their advantages compared to the conventional workbench experimental methods are explained in detail and through examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.579-580
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• 2009

Proxy Mobile IPv6에서는 같은 Local Mobility Anchor 내의 다른 Mobile Access Gateway에 있는 Mobile Node들의 패킷 전송에 있어서 발생하는 삼각 라우팅 문제는 여전히 존재한다. 이 문제점을 해결하기 위해 인터넷 드래프트 Liebsch와 Dutta에서 제안된 두 가지 Route Optimization 기법의 동작 과정을 알아보고, 상호 데이터 전송 상황에서 더 나은 성능을 제공하는 Correspondent Route Optimization 기법을 제안한다. 제안한 Route Optimization 기법은 Correspondent Flag를 추가하여 Mobile Access Gateway 간 Corresponding Binding을 완료하여, Route Optimization을 설정한다. 제안한 Correspondent Route Optimization 기법은 기존의 기법보다 상호 데이터 전송 상황에서 Route Optimization에 필요한 메시지 수가 적기 때문에 시그널링 비용이 감소하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1218-1221
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• 2009

최근 Internet Engineering Task Force에서 표준화가 된 Proxy Mobile IPv6는 기존의 이동성 보장 프로토콜인 Mobile IPv6가 가지는 많은 문제점을 보완했다. 하지만, Proxy Mobile IPv6에서 같은 Local Mobility Anchor 내에 있고, 다른 Mobile Access Gateway에 있는 Mobile Node 사이의 패킷 전송에 있어서 발생하는 삼각 라우팅 문제는 여전히 존재한다. 이 문제점을 해결하기 위해 최근 Liebsch의 드래프트와 A.Dutta의 드래프트에서 제안된 두 가지의 Route Optimization 기법의 동작 과정을 알아보고, 상호 데이터 전송 상황에서 더 나은 성능을 제공하는 새로운 Route Optimization 기법을 제안한다. 제안한 Route Optimization 기법은 Corresponding Information을 이용하여 Mobile Access Gateway 간 Corresponding Binding을 완료하여, Route Optimization을 설정한다. 제안한 Correspondent Information을 이용한 Route Optimization 기법은 기존의 기법보다 상호 데이터 전송 상황에서 Route Optimization에 필요한 메시지 수가 적기 때문에 시그널링 비용이 감소하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.857-862
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• 2004

This paper analyzes properties of the recently developed nonlinear optimization method, Dynamic Encoding Algorithm for Searches (DEAS) [1]. DEAS locates local minima with binary strings (or binary matrices for multi-dimensional problems) by iterating the two operators; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., zero or one), while UDS performs increment or decrement to binary strings with no change of string length. Owing to these search routines, DEAS retains the optimization capability that combines the special features of several conventional optimization methods. In this paper, a special feature of BSS and UDS in DEAS is analyzed. In addition, a effective global search strategy is established by using information of DEAS. Effectiveness of the proposed global search strategy is validated through the well-known benchmark functions.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.2
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• pp.112-121
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• 2013

An approach for composing a performance optimized computational code is suggested for the latest microprocessors. The concept of the code optimization, termed localization, is maximizing the utilization of the second level cache that is common to all the latest computer systems, and minimizing the access to system main memory. In this study, the localized optimization of the LU-SGS (Lower-Upper Symmetric Gauss-Seidel) code for the solution of fluid dynamic equations was carried out in three different levels and tested for several different microprocessor architectures widely used these days. The test results of localized optimization showed a remarkable performance gain of more than two times faster solution than the baseline algorithm for producing exactly the same solution on the same computer system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.