• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.55-61
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• 2009

This paper presents a modified Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm to configure Community Energy Systems(CESs) in the distribution system. The CES obtains electric power from its own Distributed Generations(DGs) and purchases insufficient power from the competitive power market, to supply power for customers contracted with the CES. When there are two or more CESs in a network, the CESs will continue the competitive expansion to reduce the total operation cost. The particles of the proposed PDPSO algorithm have magnitude and phase angle values, and move within a circle area. In the case study, the results by PDPSO algorithm was compared with that by the conventional DPSO algorithm.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.541-548
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• 2014

In this paper, we introduce a second version of modified binary particle swarm optimization using a concept of genotype-phenotype in genetic algorithms. Particle swarm optimization uses an information of difference between a position of the best solution and one's own position in the process of searching optimum. To obtain this difference of positions, the first version of modified binary particle swarm optimization uses a phenotype but the proposed second version uses a genotype. We can represent the solution space in large search space by using a genotype which provides continuous whole space as search space compared to a phenotype which provides only binary information. Experimental results in 10 De Jong benchmark function show that the second version outperforms the first version in six functions which has a broad search space and many local optima.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.86-90
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• 2007

본 논문에서는 Particle Swarm Optimization(PSO) 기법을 이용한 이족보행로봇의 보행 계획방법을 제안한다. 이족보행로봇의 보행 프리미티브를 기반으로 PSO의 학습 및 군집 특성을 이용하여 장애물이 있는 작업공간에서 보행 계획을 수행하였다. 먼저 PSO의 탐색알고리즘을 사용하여 장애물을 회피하는 실행 가능한 보행 프리미티브들의 순서를 찾아내고 탐색된 순서를 바탕으로 경로 최적화 알고리즘을 수행하는 보행 계획방법을 제안하였다. 제안된 PSO 기반 이족보행로봇의 보행 계획방법은 모의실험을 통하여 발걸음 탐색 시간이 줄고 최적화된 보행 경로를 생성하는 것을 검증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.128-133
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• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.778-779
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• 2007

기술의 진보와 친환경적 시대의 조류에 발맞추어 배전계통에서 분산전원의 이용은 더욱 증가하는 추세이다. 따라서 저용량의 다양한 분산전원을 하나의 가상발전소(Virtual Power Plant, VPP)로 운영하는 개념이 도입되고 있다. 본 논문은 Particle Swarm Optimization(PSO) 알고리즘을 이용하여 경제적 효율을 고려한 VPP의 최적구성을 다룬다. 사례연구로는 다양한 분산전원으로 구성된 시스템의 시간별 전력 및 열에너지 수요를 고려한 VPP의 최적구성을 수행한다.

• Structural Engineering and Mechanics
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• v.42 no.3
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• pp.335-352
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• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.176-179
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• 2007

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1780-1785
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• 2015

In this paper, the adaptive particle swarm optimization (APSO) algorithm is employed to design a gain-enhanced antenna with a reflector for energy harvesting. We placed the reflector below the main radiating element. Its back-radiated field is reflected and added to the forward radiated field, which could increase the antenna gain. We adopt the adaptive particle swarm optimization (APSO) algorithm, which improves the speed of convergence with a high frequency solver. The result shows that performance of the optimized design successfully satisfied the design goal of the frequency band, gain and axial ratio.