• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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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.

• 한국전자파학회논문지
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• 제14권5호
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• pp.499-505
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• 2003

이동 무선 통신 시스템을 설계할 때 기지국의 위치는 매우 중요한 파라미터 중 하나이다. 기지국 위치를 설계할 때 여러 가지 복잡한 변수들을 잘 조합하여 코스트가 최소가 되도록 설계해야 한다. 이러한 문제를 해결하는데 필요한 알고리즘이 조합 최적화 알고리즘이며, 지금까지 조합 최적화 기술로 Random Walk, Simulated Annealing, Tabu Search, Genetic Algorithm과 같은 전역 최적화 기술이 사용되어 왔다. 본 논문은 이동 통신시스템의 기지국 위치 최적화에 위의 4가지 알고리즘들을 적용하여 각 알고리즘의 결과를 비교 분석하며 알고리즘에 의한 최적화 과정을 보여준다.

• Journal of electromagnetic engineering and science
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• 제19권1호
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• pp.31-36
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• 2019

In this study, an effective method for designing an electromagnetic multilayer radome is introduced. This method is achieved by using ant colony optimization for a continuous domain in the transmission coefficient maximization with stability for a wide angle of incidence in both perpendicular and parallel polarizations in specific X- and Ku-bands. To obtain the optimized parameter for a C-sandwich radome, particle swarm optimization algorithm is operated to give a clear comparison on the effectiveness of ant colony optimization for a continuous domain. The qualification of an optimized multilayer radome is also compared with an effective solid radome type in transmitted power stability and presented in this research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1301-1318
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• 2017

In order to utilize the licensed channel of cognitive radio (CR) when the primary user (PU) is detected busy, a benefit-exchange access mode based on cooperative communication is proposed to allow secondary user (SU) to access the busy channel through giving assistance to PU's communication in exchange for some transmission bandwidth. A holistic joint optimization problem is formulated to maximize the total throughput of CR system through jointly optimizing the parameters of cooperative spectrum sensing (CSS), including the local sensing time, the pre-configured sensing decision threshold, the forward power of cooperative communication, and the bandwidth and transmission power allocated to SUs in benefit-exchange access mode and traditional access mode, respectively. To solve this complex problem, a combination of bi-level optimization, interior-point optimization and exhaustive optimization is proposed. Simulation results show that, compared with the tradition throughput maximizing model (TTMM), the proposed holistic joint optimization model (HJOM) can make use of the channel effectively even if PU is busy, and the total throughput of CR obtains a considerable improvement by HJOM.

• 한국컴퓨터정보학회논문지
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• 제9권1호
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• pp.87-93
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• 2004

이동 무선 통신 네트워크를 설계할 때 기지국(BTS), 기지국 콘트롤러(BSC), 이동 교환국(MSC)의 위치는 매우 중요한 파라미터들이다. 기지국의 위치를 설계할 때는 여러 가지 복잡한 변수들을 잘 조합하여 비용이 최소가 되도록 설계해야 한다 이러한 문제를 해결하는데 필요한 알고리듬이 전역 최적화 알고리듬이며, 지금까지 전역 최적화 검색 기술로는 Random Walk, Simulated Annealing, Tabu Search, Genetic Algorithm이 사용되어 왔다. 본 논문은 이동 통신 시스템의 기지국, 기지국 콘트롤러, 이동 교환국의 위치 최적화에 위의 4가지 알고리듬들을 적용하여 각 알고리듬의 결과를 비교 분석하며 알고리듬에 의한 최적화 과정을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.564-582
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• 2015

This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step for the sake of driving the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. Numerical results show that the proposed QBFO has more powerful properties in terms of convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. Furthermore, we examine the employment of our proposed QBFO for cognitive radio spectrum allocation. The results indicate that the proposed QBFO based spectrum allocation scheme achieves high efficiency of spectrum usage and improves the transmission performance of secondary users, as compared to color sensitive graph coloring algorithm and quantum genetic algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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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.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.392-400
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• 2008

This paper proposes a novel optimization algorithm inspired by bacteria behavior patterns for foraging. Most bacteria can trace attractant chemical molecules for foraging. This tracing capability of bacteria called chemotaxis might be optimized for foraging because it has been evolved for few millenniums. From this observation, we developed a new optimization algorithm based on the chemotaxis of bacteria in this paper. We first define behavior and decision rules based on the behavior patterns of bacteria and then devise an optimization algorithm with these behavior and decision rules. Generally bacteria have a quorum sensing mechanism that makes it possible to effectively forage, but we leave its implementation as a further work for simplicity. Thereby, we call our algorithm a simple bacteria cooperative optimization (BCO) algorithm. Our simple BCO is tested with four function optimization problems on various' parameters of the algorithm. It was found from experiments that the simple BCO can be a good framework for optimization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.188-210
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• 2022

As a promising technique to support tremendous numbers of Internet of Things devices and a variety of applications efficiently, mobile edge computing (MEC) has attracted extensive studies recently. In this paper, we consider a MEC-assisted peer-to-peer (P2P) mobile communication system where MEC servers are deployed at access points to accelerate the communication process between mobile terminals. To capture the tradeoff between the time delay and the energy consumption of the system, a cost function is introduced to facilitate the optimization of the computation and communication resources. The formulated optimization problem is non-convex and is tackled by an iterative block coordinate descent algorithm that decouples the original optimization problem into two subproblems and alternately optimizes the computation and communication resources. Moreover, the MEC-assisted P2P communication system is compared with the conventional P2P communication system, then a condition is provided in closed-form expression when the MEC-assisted P2P communication system performs better. Simulation results show that the advantage of this system is enhanced when the computing capability of the receiver increases whereas it is reduced when the computing capability of the transmitter increases. In addition, the performance of this system is significantly improved when the signal-to-noise ratio of hop-1 exceeds that of hop-2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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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.