• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4987-5005
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• 2017

This paper introduces a new method to solve Similarity Recognition for Cloth Products, which is based on Fuzzy logic and Particle swarm optimization algorithm. For convenience, it is called the SRCPFP method hereafter. In this paper, the SRCPFP method combines Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) algorithm to solve similarity recognition for cloth products. First, it establishes three features, length, thickness, and temperature resistance, respectively, for each cloth product. Subsequently, these three features are engaged to construct a Fuzzy Inference System (FIS) which can find out the similarity between a query cloth and each sampling cloth in the cloth database D. At the same time, the FIS integrated with the PSO algorithm can effectively search for near optimal parameters of membership functions in eight fuzzy rules of the FIS for the above similarities. Finally, experimental results represent that the SRCPFP method can realize a satisfying recognition performance and outperform other well-known methods for similarity recognition under considerations here.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1329-1339
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• 2017

This paper presents the power scheduling of a multi-microgrid (MMG) system using an optimization technique called particle swarm optimization (PSO). The PSO technique has been shown to be most effective at solving the various problems of the economic dispatch (ED) in a power system. In addition, a new MMG system configuration is proposed in this paper, through which the optimal power flow is achieved. Both optimization and power trading methods within an MMG are studied. The results of implementing PSO in an MMG system for optimal power flow and cost minimization are obtained and compared with another attractive and efficient optimization technique called the genetic algorithm (GA). The comparison between these two effective methods provides very competitive results, and their operating costs also appear to be comparable. Finally, in this study, power scheduling and a power trading method are obtained using the MATLAB program.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.131-138
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• 2022

A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.

• 대한전자공학회논문지TC
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• 제46권1호
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• pp.1-9
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• 2009

OFDM(Orthogonal Frequency Division Multiplexing) 시스템은 주파수 선택적 페이딩(frequency selective fading)과 협대역 간섭(narrowband interference)에 강한 전송 방식으로 대용량 데이터 통신에 적합하다. 하지만 독립적으로 변조된 많은 부반송파들의 중첩으로 신호의 진폭이 증가하여 PAPR(Peak-to-Average Power Ratio)이 증가하는 문제가 발생한다. PAPR 문제를 해결하기 위해 제안된 PTS(Partial Transmit Sequence) 기법은 OFDM 신호를 부블록으로 나눈 후 위상 가중치를 곱하여 PAPR을 감소시킬 수 있지만, 위상 가중치를 탐색하는 과정에서 계산의 복잡도가 부블록 수에 따라 지수적으로 증가하는 단점이 있다. 본 논문에서는 PTS 기법의 위상 탐색 과정에 최적화 기법인 변형된 Greedy 알고리즘과 PSO(Particle Swarm Optimization) 알고리즘을 조합한 MG-PSO(Modified Greedy algorithm-Particle Swarm Optimization) 알고리즘을 적용한 구조를 제안하였다. 이 구조는 PTS 기법의 위상 탐색 과정에서 계산 복잡도가 지수적으로 증가하는 문제를 해결하고 PAPR 감소 성능도 보장할 수 있다. 제안하는 알고리즘을 통신 시스템에 적용하였을 때 PAPR 감소 성능을 분석하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2574-2587
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• 2019

Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

• 한국항행학회논문지
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• 제24권4호
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• pp.291-298
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• 2020

본 연구는 입자 무리 최적화 (PSO; particle swarm optimization) 알고리즘을 이용하여 기존의 MPPT 알고리즘보다 신속하게 MPP를 추적할 수 있는 모델을 제안하였다. 제안 모델은 PSO 알고리즘에서 gbest 및 pbest의 가속 상수를 높게 설정하여 신속하게 MPP 지점을 추적하고 이로 인한 전력 불안정 문제점을 제거하였다. 또한, 일사량의 급격한 변화에 따른 태양광 패널의 전력 변화를 감지하여 알고리즘을 다시 실행하였다. 실험결과, 일사량이 691.5W/m2에 대해서 MPPT 시간이 0.03초와 전력이 131.65로서 기존의 P&O와 INC 알고리즘보다 높은 전력과 빠른 속도로 MPP를 추적하였으며, 일사량 변화에 따라 신속하게 MPP를 추적하였다. 제안 모델은 태양광 패널이 병렬로 연결되어 있는 태양광 발전소에서 부분적인 음영에 의해 전력량의 변화를 감지하였을 경우에도 적용할 수 있다. 본 연구는 MPPT 알고리즘을 개선하기 위해 MFO (moth flame optimization) 및 WOA (whale optimization algorithm)와 같은 최적화 알고리즘에 대한 비교 연구가 필요하다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.181-182
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• 2008

본 논문에서는 연산 최적화 알고리듬 중 PSO(Particle Swarm Optimization) 알고리듬을 NVIDIA사(社)에서 제공한 CUDA(Compute Unified Device Architecture)를 이용하여 새롭게 구현하였다. CUDA는 CPU가 아닌 GPU(Graphic Processing Unit)의 다양한 병렬 처리 능력을 사용해 복잡한 컴퓨팅 문제를 해결하는 소프트웨어 개발을 가능케 하는 기술이다. 이 기술을 연산 최적화 알고리듬 중 PSO에 적용함으로써 알고리듬의 수행 속도를 개선하였다. CUDA를 적용한 PSO 알고리듬의 검증을 위해 언어 기반으로 프로그래밍하고 다양한 Test Function을 통해 시뮬레이션 하였다. 그리고 기존의 PSO 알고리듬과 비교 분석하였다. 또한 알고리듬의 성능 향상으로 여러 가지 최적화 분야에 적용 할 수 있음을 보인다.

• 지구물리와물리탐사
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• 제22권4호
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• pp.186-194
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• 2019

물리탐사 자료의 역산 해를 찾는데 흔히 이용되는 결정론적 해법은 지역 최소점에 빠져 적절한 해에 수렴하지 못할 가능성이 크다는 단점이 존재한다. 이 문제를 해결하기 위한 대안 중 하나는 확률론적 접근법에 기반한 전역 최적화 방법을 이용하는 것이며, 여러 방법들 중에서 입자 군집 최적화(Particle Swarm Optimization, PSO)법의 적용사례가 많이 소개되었다. 이 논문에서는 PSO법을 이용한 소형루프 전자탐사 자료의 층서 구조 전기비저항 역해석 알고리즘을 개발하고 합성자료를 이용하여 역산실험을 수행하였다. 실험결과 기존의 Gauss-Newton 알고리즘으로는 최적의 역산해를 찾는데 어려움이 있는 소형루프 전자탐사 자료의 역산 시도에 PSO 방법을 적용하면 성공률을 높일 수 있음을 확인하였다.

• Structural Engineering and Mechanics
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• 제42권6호
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.