• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권2호
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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.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2008년도 추계 공동 국제학술대회
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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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].

• Structural Engineering and Mechanics
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• 제47권4호
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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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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• 제10권4호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.384-386
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• 2009

This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.