• Title/Summary/Keyword: Artificial Bee Colony Algorithm

Search Result 51, Processing Time 0.027 seconds

River stage forecasting models using support vector regression and optimization algorithms (Support vector regression과 최적화 알고리즘을 이용한 하천수위 예측모델)

  • Seo, Youngmin;Kim, Sungwon
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • 2015.05a
    • /
    • pp.606-609
    • /
    • 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.

  • PDF

Improved Artificial Bee Clustering (ABC) Algorithm for Solving Consistency Problems in SDN Distributed Controllers (SDN 분산 컨트롤러에서 일관성 문제 해결을 위한 향상된 인공벌 군집(ABC) 알고리즘)

  • Yoo, Seung-Eon;Lym, Hwan-Hee;Lee, Byung-Jun;Kim, Kyung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
    • /
    • 2018.07a
    • /
    • pp.145-146
    • /
    • 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.

  • PDF

Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

  • KHALFA, Ali;AMARDJIA, Nourredine;KENANE, Elhadi;CHIKOUCHE, Djamel;ATTIA, Abdelouahab
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.13 no.5
    • /
    • pp.2574-2587
    • /
    • 2019
  • Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

Design of Robust Face Recognition System with Illumination Variation Realized with the Aid of CT Preprocessing Method (CT 전처리 기법을 이용하여 조명변화에 강인한 얼굴인식 시스템 설계)

  • Jin, Yong-Tak;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.25 no.1
    • /
    • pp.91-96
    • /
    • 2015
  • In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

A Rendezvous Node Selection and Routing Algorithm for Mobile Wireless Sensor Network

  • Hu, Yifan;Zheng, Yi;Wu, Xiaoming;Liu, Hailin
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.12 no.10
    • /
    • pp.4738-4753
    • /
    • 2018
  • Efficient rendezvous node selection and routing algorithm (RNSRA) for wireless sensor networks with mobile sink that visits rendezvous node to gather data from sensor nodes is proposed. In order to plan an optimal moving tour for mobile sink and avoid energy hole problem, we develop the RNSRA to find optimal rendezvous nodes (RN) for the mobile sink to visit. The RNSRA can select the set of RNs to act as store points for the mobile sink, and search for the optimal multi-hop path between source nodes and rendezvous node, so that the rendezvous node could gather information from sensor nodes periodically. Fitness function with several factors is calculated to find suitable RNs from sensor nodes, and the artificial bee colony optimization algorithm (ABC) is used to optimize the selection of optimal multi-hop path, in order to forward data to the nearest RN. Therefore the energy consumption of sensor nodes is minimized and balanced. Our method is validated by extensive simulations and illustrates the novel capability for maintaining the network robustness against sink moving problem, the results show that the RNSRA could reduce energy consumption by 6% and increase network lifetime by 5% as comparing with several existing algorithms.

Relay Selection Scheme Based on Quantum Differential Evolution Algorithm in Relay Networks

  • Gao, Hongyuan;Zhang, Shibo;Du, Yanan;Wang, Yu;Diao, Ming
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.11 no.7
    • /
    • pp.3501-3523
    • /
    • 2017
  • It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

Design of optimal PID controller for the reverse osmosis using teacher-learner-based-optimization

  • Rathore, Natwar S.;Singh, V.P.
    • Membrane and Water Treatment
    • /
    • v.9 no.2
    • /
    • pp.129-136
    • /
    • 2018
  • In this contribution, the control of multivariable reverse osmosis (RO) desalination plant using proportional-integral-derivative (PID) controllers is presented. First, feed-forward compensators are designed using simplified decoupling method and then the PID controllers are tuned for flux (flow-rate) and conductivity (salinity). The tuning of PID controllers is accomplished by minimization of the integral of squared error (ISE). The ISEs are minimized using a recently proposed algorithm named as teacher-learner-based-optimization (TLBO). TLBO algorithm is used due to being simple and being free from algorithm-specific parameters. A comparative analysis is carried out to prove the supremacy of TLBO algorithm over other state-of-art algorithms like particle swarm optimization (PSO), artificial bee colony (ABC) and differential evolution (DE). The simulation results and comparisons show that the purposed method performs better in terms of performance and can successfully be applied for tuning of PID controllers for RO desalination plants.

Constrained Relay Node Deployment using an improved multi-objective Artificial Bee Colony in Wireless Sensor Networks

  • Yu, Wenjie;Li, Xunbo;Li, Xiang;Zeng, Zhi
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.11 no.6
    • /
    • pp.2889-2909
    • /
    • 2017
  • Wireless sensor networks (WSNs) have attracted lots of attention in recent years due to their potential for various applications. In this paper, we seek how to efficiently deploy relay nodes into traditional static WSNs with constrained locations, aiming to satisfy specific requirements of the industry, such as average energy consumption and average network reliability. This constrained relay node deployment problem (CRNDP) is known as NP-hard optimization problem in the literature. We consider addressing this multi-objective (MO) optimization problem with an improved Artificial Bee Colony (ABC) algorithm with a linear local search (MOABCLLS), which is an extension of an improved ABC and applies two strategies of MO optimization. In order to verify the effectiveness of the MOABCLLS, two versions of MO ABC, two additional standard genetic algorithms, NSGA-II and SPEA2, and two different MO trajectory algorithms are included for comparison. We employ these metaheuristics on a test data set obtained from the literature. For an in-depth analysis of the behavior of the MOABCLLS compared to traditional methodologies, a statistical procedure is utilized to analyze the results. After studying the results, it is concluded that constrained relay node deployment using the MOABCLLS outperforms the performance of the other algorithms, based on two MO quality metrics: hypervolume and coverage of two sets.

Design of RBFNN-Based Pattern Classifier for the Classification of Precipitation/Non-Precipitation Cases (강수/비강수 사례 분류를 위한 RBFNN 기반 패턴분류기 설계)

  • Choi, Woo-Yong;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.24 no.6
    • /
    • pp.586-591
    • /
    • 2014
  • In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

Design of Fuzzy Clustering-based Neural Networks Classifier for Sorting Black Plastics with the Aid of Raman Spectroscopy (라만분광법에 의한 흑색 플라스틱 선별을 위한 퍼지 클러스터링기반 신경회로망 분류기 설계)

  • Kim, Eun-Hu;Bae, Jong-Soo;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.66 no.7
    • /
    • pp.1131-1140
    • /
    • 2017
  • This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.