• 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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.682-685
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• 2014

본 논문에서는 센서노드용 원형편파 안테나의 설계하였다. 확률론적 방법인 Particle Swarm Optimization(PSO) 알고리즘과 Adaptive Particle Swam Optimization(APSO) 알고리즘을 구현하고 성능비교를 통해 안테나 최적설계에 적합한 알고리즘을 결정하였다. PSO는 41번, APSO는 27번의 계산 결과 수렴을 하였다. 두 알고리즘 모두 최적설계에서 목표값을 모두 만족을 하였으나 수렴도에서 APSO가 빠르게 수렴한 것을 확인할 수 있었다.

• 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.

• 한국전자통신학회논문지
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• 제13권3호
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• pp.587-592
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• 2018

위상 배열 안테나를 이용한 적응형 빔 형성 시스템은 간섭신호가 있는 통신환경에 적응형으로 빔을 형성하여 통신 품질을 향상시킨다. 적응형 빔 형성을 위해서는 위상 배열 안테나의 각 방사소자에 급전되는 신호의 위상을 우수한 조합을 산출해야 한다. 본 논문에서는 우수한 위상 천이 조합 산출 확률을 증가시키기 위해, 개치 밀도에 따른 재확산 절차가 추가된 개선된 개체 군집 최적화 알고리즘을 제안하였다.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2019-2024
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• 2014

This paper presents a method to optimize the design of a planar-type antenna and reduce the number of design parameters for rapid computation. The electromagnetic characteristics of the structure are analyzed, and Taguchi method is used to identify critical design parameters. Adaptive particle swarm optimization, which has a faster convergence rate than particle swarm optimization, is used to achieve the design goal effectively. A compact dual-band USB dongle antenna is tested to verify the advantage of the proposed method. In this case, we can use only five selected geometrical parameters instead of eighteen to accelerate the optimization of the antenna design. The 10 dB bandwidth for return loss ranges from 2.3 GHz to 2.7 GHz and from 5.1 GHz to 5.9 GHz, covering all the WiBro, Bluetooth, WiMAX, and 802.11 b/g/n WLAN bands in both simulation and measurement. The optimization process enables the antenna design to achieve the required performance with fewer design parameters.

• Nuclear Engineering and Technology
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• 제48권6호
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• pp.1315-1320
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• 2016

The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

• Industrial Engineering and Management Systems
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• 제13권1호
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• pp.43-51
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• 2014

This study presents an application of adaptive particle swarm optimization (APSO) to solving the bi-level job-shop scheduling problem (JSP). The test problem presented here is $10{\times}10$ JSP (ten jobs and ten machines) with tribottleneck machines formulated as a bi-level formulation. APSO is used to solve the test problem and the result is compared with the result solved by basic PSO. The results of the test problem show that the results from APSO are significantly different when compared with the result from basic PSO in terms of the upper level objective value and the iteration number in which the best solution is first identified, but there is no significant difference in the lower objective value. These results confirmed that the quality of solutions from APSO is better than the basic PSO. Moreover, APSO can be used directly on a new problem instance without the exercise to select parameters.

• 전기학회논문지
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• 제62권11호
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• pp.1578-1583
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• 2013

In this paper, stochastic optimization algorithms of PSO (Particle Swarm Optimization) and APSO (Adaptive Particle Swam Optimization) are studied and compared. It is revealed that the APSO provides faster convergence and better search efficiency than the conventional PSO when they are adopted to find the global minimum of a two-dimensional function. The advantages of the APSO comes from the ability to control the inertia weight, and acceleration coefficients. To verify that the APSO is working better than the standard PSO, the design of a 10GHz microstrip patch as one of the elements of a high frequency array antenna is taken as a test-case and shows the optimized result with 5 iterations in the APSO and 28 iterations in th PSO.

• Journal of Power Electronics
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• 제18권5호
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• pp.1470-1478
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• 2018

Recently, wireless power transfer (WPT) via coupled magnetic resonances has attracted a lot of attention owing to its long operation distance and high efficiency. However, the WPT systems is over-coupling and a frequency splitting phenomenon occurs when resonators are placed closely, which leads to a decrease in the transfer power. To solve this problem, an adaptive frequency tracking control (AFTC) was used based on a closed-loop control scheme. An improved particle swarm optimization (PSO) algorithm was proposed with the AFTC to track the maximum power point in real time. In addition, simulations were carried out. Finally, a WPT system with the AFTC was demonstrated to experimentally validate the improved PSO algorithm and its tracking performance in terms of optimal frequency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5955-5977
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• 2018

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.