• Structural Engineering and Mechanics
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• 제73권4호
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• pp.463-479
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• 2020

This paper is aimed to address a simultaneous optimization of the size, shape, and topology of steel lattice towers through a combination of the radial basis function (RBF) neural networks and the artificial bee colony (ABC) metaheuristic algorithm to reduce the computational time because mere metaheuristic optimization algorithms require much time for calculations. To verify the results, use has been made of the CIGRE Tower and a 132 kV transmission towers as numerical examples both based on the design requirements of the ASCE10-97, and the size, shape, and topology have been optimized (in both cases) once by the RBF neural network and once by the MSTOWER analyzer. A comparison of the results shows that the neural network-based method has been able to yield acceptable results through much less computational time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.52-68
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• 2019

Aiming at the optimal placement of communication relay nodes (OPCRN) problem in manned/unmanned aerial vehicle cooperative engagement system, this paper designed a kind of fully connected broadband backbone communication topology. Firstly, problem description of OPCRN was given. Secondly, based on problem analysis, the element attributes and decision variables were defined, and a bi-level programming model including physical layer and logical layer was established. Thirdly, a hierarchical artificial bee colony (HABC) algorithm was adopted to solve the model. Finally, multiple sets of simulation experiments were carried out to prove the effectiveness and superiority of the algorithm.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.39-40
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• 2019

본 논문에서는 연관규칙 마이닝 알고리즘인 Apriori 알고리즘을 기반으로 향상된 인공벌 군집 알고리즘(ABC algorihtm)을 적용하여 SDN 환경에서 분산된 컨트롤러를 선택하는 모델을 제안하였다. 이를 통해 자주 사용되는 컨트롤러를 우선적으로 선택함으로써 향상된 컨트롤러 선택을 목표로 한다.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.735-740
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• 2012

본 연구에서는 주성분 분석법 및 선형 판별 분석법을 이용한 다항식 방사형 기저 함수 신경회로망 분류기의 설계 방법론을 소개한다. 주성분 분석법과 선형판별 분석법을 사용하여 주어진 데이터의 정보 손실을 최소화한 특징데이터를 생성하고 이를 다항식 방사형 기저함수 신경회로망의 입력데이터로 사용한다. 방사형 기저 함수 신경회로망의 은닉층은 FCM 클러스터링 알고리즘으로 구성되며 연결가중치는 1차 선형식을 사용하였다. 최적의 분류기 설계를 위해서 최근에 제안된 Artificial Bee Colony(ABC) 최적화 알고리즘을 사용하여 구조 및 파라미터를 동조하였다. ABC 알고리즘을 통해 주성분 분석법과 선형판별 분석법의 고유벡터의 수 및 FCM 클러스터링 알고리즘의 퍼지화 계수등의 파라미터를 동조한다. 제안된 분류기는 대표적인 Machine Learning(ML) 데이터를 사용하여 성능을 평가하며 기존 분류기와 성능을 비교한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.606-609
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• 2015

본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

• Journal of Power Electronics
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• 제16권4호
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• pp.1438-1454
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• 2016

Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

• Steel and Composite Structures
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• 제24권1호
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• pp.93-112
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• 2017

Metaheuristic algorithms in general make use of uniform random numbers in their search for optimum designs. Levy Flight (LF) is a random walk consisting of a series of consecutive random steps. The use of LF instead of uniform random numbers improves the performance of metaheuristic algorithms. In this study, three discrete optimum design algorithms are developed for steel skeletal structures each of which is based on one of the recent metaheuristic algorithms. These are biogeography-based optimization (BBO), brain storm optimization (BSO), and artificial bee colony optimization (ABC) algorithms. The optimum design problem of steel skeletal structures is formulated considering LRFD-AISC code provisions and W-sections for frames members and pipe sections for truss members are selected from available section lists. The minimum weight of steel structures is taken as the objective function. The number of steel skeletal structures is designed by using the algorithms developed and effect of LF is investigated. It is noticed that use of LF results in up to 14% lighter optimum structures.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.145-146
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• 2018

중앙 집중적인 단일 컨트롤러를 이용할 경우 메시지 과부하로 인해 응답이 지연될 수 있으므로 스위치들이 기존의 컨트롤러를 대신하여 새로운 컨트롤러와 연결되어 트래픽을 처리하는 다중 컨트롤러가 효율적이다. 본 논문에서는 SDN 분산 컨트롤러에서 일관성 문제를 해결하기 위해 우선순위에 기반을 둔 향상된 인공벌 군집(ABC) 알고리즘을 제안한다.

• 대한수의학회지
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• 제16권1호
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• pp.11-25
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• 1976

Studies on pathogenicities and developmental stages of Nosema apis (Zander, 1909) were carried out through artificial infection to Nosema free honey bees with various levels of spores isolated from local honey bee colony. The results obtained were summarized as follows: 1. The clinical symptoms were observed as dysentery, enteritis of mid-gut (enlargement and decoloration), crawling posture and shortening of the longevity of worker bees in the rearing honey bee colony inoculated with the spores. 2. Number of spores harvested from laboratory rearing honey bees were progresively increased to 4 weeks after inoculation. The regression equations and coefficients of correlations to various spore levels were as follows in each treatment colony. Colony 1. ($$1,000{\times}10^4spores/ml$$) $$y_{c1}=471{\times}10^{4}x+454{\times}10^4(r=0.65^*$$) Colony 2. ($$500{\times}10^4spores/ml$$) $$y_{c2}=340{\times}10^{4}x+207.8{\times}10^4(r=0.99^{**}$$) Colony 3. ($$100{\times}10^4spores/ml$$) $$y_{c3}=150{\times}10^{4}x+84.2{\times}10^4(r=0.99^{**}$$) Colony 4. ($$10{\times}10^4spores/ml$$) $$y_{c4}=13.8{\times}10^{4}x+13{\times}10^4(r=0.98^{**}$$) 3. Average longevity of worker bees artificially infected with Nosema apis was shortened as 21.7~43.8% compare to the control. (p<.05, p<.01) 4. The spores which were isolated from honey bee colony infected with Nosema disease were ovoid or spherical form, and measured, as a rule, from $4.7{\mu}m$ to $6.1{\mu}m$ (mean $5.3{\mu}m$) in length and from $2.4{\mu}m$ to $3.2{\mu}m$ (mean $2.9{\mu}m$) in width. 5. In the mid-gut of honey bees, the spore was progresively germinated and became trophozoite stage. The trophozoites were grown to meronts and their binary fission were begun. The divided two sporoblasts were developed to the spores which had elastic membrane. The new spores were shed in excreta of honey bees 10~15 day after inoculation at $25{\pm}2$ centigrade. 6. The ultrastructure of spore membrane consisted of three layers, such as, outer, middle and inner layer. The sporoplasm consisting lamellar structure occupied only anterior part of the spore and was often extended to posterior direction where definite vacuoles and a polar filament was able to detect.