• The KIPS Transactions:PartD
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• v.17D no.3
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• pp.175-184
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• 2010

Experiments and research on genes have become very convenient by using DNA chips, which provide large amounts of data from various experiments. The data provided by the DNA chips could be represented as a two dimensional matrix, in which one axis represents genes and the other represents samples. By performing an efficient and good quality clustering on such data, the classification work which follows could be more efficient and accurate. In this paper, we use a bio-inspired algorithm called the Particle Swarm Optimization algorithm to propose an efficient clustering mechanism for large amounts of DNA chip data, and show through experimental results that the clustering technique using the PSO algorithm provides a faster yet good quality result compared with other existing clustering solutions.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.87-92
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• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1026-1028
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• 2005

본 논문에서는 PSO(Particle Swarm Optimization)에 기초하여 multi-modal 최적화를 위한 다중 그룹 Particle Swarm 최적화 알고리즘(MGPSO)을 제안하였다. 제안된 알고리즘은 PSO의 기본 특성을 유지하기 때문에 기존의 혼합형 타입의 최적화 방식에 비하여 빠른 수렴 시간을 가지며 구성방식이 간단하다. 여러 개의 피크를 가지는 테스트 함수를 통해 본 논문에서 제시한 알고리즘의 타당성을 입증하였으며, 영구자석 매입형 전동기의 최적 설계에 적용하여 그 유용성을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.682-685
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• 2014

In this paper, an improved designed of the circularly polarization antenna for sensor node. Stochastic optimization algorithms of Particle Swarm Optimization (PSO) and Adaptive Particle Swam Optimization(APSO) are studied and compared. To verify that the APSO is working better than the standard PSO, the design of a circularly polarization antenna is shows the optimized result with 27 iterations in the APSO and 41 iterations in th PSO.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

• 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.

• 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.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.549-557
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• 2014

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.163-170
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• 2011

This paper proposes a modified version of Particle Swarm Optimization (PSO) called Information Diffusion PSO (ID-PSO). In PSO algorithms, premature convergence of particles could be prevented by defining proper population topology. In this paper, we propose a variant of PSO algorithm using a new population topology. We draw inspiration from the theory of information diffusion which models the transmission of information or a rumor as one-to-one interactions between people. In ID-PSO, a particle interacts with only one particle at each iteration and they share their personal best solutions and recognized best solutions. Each particle recognizes the best solution that it has experienced or has learned from another particle as the recognized best. Computational experiments on the benchmark functions show the effectiveness of the proposed algorithm compared with the existing methods which use different population topologies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.185-188
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• 2009

In this paper, Position Controller for balance of Seesaw System design using PID Algorithm. Seesaw System is that it's system use widely to analyze of ship or flight dynamics, Inverted Pendulumand, Robot System, manage system for theory of modern control system and all sorts of analysis. In case of Seesaw System, it's necessity that understand and analysis of system and correct selection of parameter because the system is strong nonlinear control system. It guarantees efficiency and stability to adapt quickly for disturbance or change of controller from PID Algorithm of guarantee safe from simple and long history and PSO(Particle Swarm Optimization) that sort of metaheuristic optimization that need to accuracy and fast PID parameter tuning.