• 한국공간구조학회논문집
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• 제21권2호
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• pp.89-97
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• 2021

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3683-3703
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• 2018

Wireless sensor networks (WSNs) consist of set of sensor nodes. These sensor nodes are deployed in unattended area which are able to sense, process and transmit data to the base station (BS). One of the primary issues of WSN is energy efficiency. In many existing clustering approaches, initial centroids of cluster heads (CHs) are chosen randomly and they form unbalanced clusters, results more energy consumption. In this paper, an energy efficient clustering protocol to prevent unbalanced clusters based on firefly and midpoint algorithms called EEC-FM has been proposed, where midpoint algorithm is used for initial centroid of CHs selection and firefly is used for cluster formation. Using residual energy and Euclidean distance as the parameters for appropriate cluster formation of the proposed approach produces balanced clusters to eventually balance the load of CHs and improve the network lifetime. Simulation result shows that the proposed method outperforms LEACH-B, BPK-means, Park's approach, Mk-means, and EECPK-means with respect to balancing of clusters, energy efficiency and network lifetime parameters. Simulation result also demonstrate that the proposed approach, EEC-FM protocol is 45% better than LEACH-B, 17.8% better than BPK-means protocol, 12.5% better than Park's approach, 9.1% better than Mk-means, and 5.8% better than EECPK-means protocol with respect to the parameter half energy consumption (HEC).

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.487-495
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• 2015

This paper proposes a new robust decentralized power oscillation dampers (POD) design of doubly-fed induction generator (DFIG) wind turbine for damping of low frequency electromechanical oscillations in an interconnected power system. The POD structure is based on the practical $2^{nd}$-order lead/lag compensator with single input. Without exact mathematical model, the inverse output multiplicative perturbation is applied to represent system uncertainties such as system parameters variation, various loading conditions etc. The parameters optimization of decentralized PODs is carried out so that the stabilizing performance and robust stability margin against system uncertainties are guaranteed. The improved firefly algorithm is applied to tune the optimal POD parameters automatically. Simulation study in two-area four-machine interconnected system shows that the proposed robust POD is much superior to the conventional POD in terms of stabilizing effect and robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2680-2696
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• 2018

The collaborative content caching system is an effective solution developed in recent years to reduce transmission delay and network traffic. In order to decrease the service end-to-end transmission delay for future 5G ultra-dense networks (UDN), this paper proposes a novel service migration method that can guarantee the continuity of service and simultaneously reduce the traffic flow in the network. In this paper, we propose a service migration optimization model that minimizes the cumulative transmission delay within the constraints of quality of service (QoS) guarantee and network condition. Subsequently, we propose an improved firefly algorithm to solve this optimization problem. Simulation results show that compared to traditional collaborative content caching schemes, the proposed algorithm can significantly decrease transmission delay and network traffic flow.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.791-800
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• 2016

Optimal Power Flow (OPF) is an important operational and planning problem in minimizing the chosen objective functions of the power systems. The recent developments in power electronics have enabled introduction of dc links in the AC power systems with a view of making the operation more flexible, secure and economical. This paper formulates a new OPF to embrace dc link equations and presents a heuristic optimization technique, inspired by the behavior of fireflies, for solving the problem. The solution process involves AC/DC power flow and uses a self adaptive technique so as to avoid landing at the suboptimal solutions. It presents simulation results of IEEE test systems with a view of demonstrating its effectiveness.

• Advances in aircraft and spacecraft science
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• 제7권4호
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• pp.353-369
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• 2020

This paper presents the flutter analysis and optimum design of axially functionally graded box beam cantilever wing section by considering various geometric and material parameters. The coupled dynamic equations of the continuous model of wing system in terms of material and cross-sectional properties are formulated based on extended Hamilton's principle. By expressing the lift and pitching moment in terms of plunge and pitch displacements, the resultant two continuous equations are simplified using Galerkin's reduced order model. The flutter velocity is predicted from the solution of resultant damped eigenvalue problem. Parametric studies are conducted to know the effects of geometric factors such as taper ratio, thickness, sweep angle as well as material volume fractions and functional grading index on the flutter velocity. A generalized surrogate model is constructed by training the radial basis function network with the parametric data. The optimized material and geometric parameters of the section are predicted by solving the constrained optimal problem using firefly metaheuristics algorithm that employs the developed surrogate model for the function evaluations. The trapezoidal hollow box beam section design with axial functional grading concept is illustrated with combination of aluminium alloy and aluminium with silicon carbide particulates. A good improvement in flutter velocity is noticed by the optimization.

• ETRI Journal
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• 제39권6호
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• pp.782-793
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• 2017

This paper considers the use of the Partial Transmit Sequence (PTS) technique to reduce the Peak-to-Average Power Ratio (PAPR) of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS) is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search-based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5904-5927
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• 2019

Many artificial intelligence (AI) techniques have been embedded into various engineering technologies to assist them in achieving different goals. The integration of modern technologies with energy consumption management system and occupant's comfort inside buildings results in the introduction of intelligent building concept. The major aim of this integration is to manage the energy consumption effectively and keeping the occupant satisfied with the internal environment of the building. The last few couple of years have seen many applications of AI techniques for optimizing the energy consumption with maximizing the user comfort in smart buildings but still there is much room for improvement in this area. In this paper, a hybrid of two AI algorithms called firefly algorithm (FA) and genetic algorithm (GA) has been used for user comfort maximization with minimum energy consumption inside smart building. A complete user friendly system with data from various sensors, user, processes, power control system and different actuators is developed in this work for reducing power consumption and increase the user comfort. The inputs of optimization algorithms are illumination, temperature and air quality sensors' data and the user set parameters whereas the outputs of the optimization algorithms are optimized parameters. These optimized parameters are the inputs of different fuzzy controllers which change the status of different actuators according to user satisfaction.

• 한국정보통신학회논문지
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• 제17권3호
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• pp.575-583
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• 2013

애드 혹 네트워크에서 반딧불 영감을 받은 접근 방식을 사용하는 분산 타이밍 동기 기술을 연구한다. 노드가 직교주파수분할다중접속 무선 인터페이스에서 통신하는 패킷 기반 통신을 하는 다중 반송파 시스템에 잘 적용될 수 있도록, 펄스의 결합진동자 이론에 기반한 반딧불 동기 알고리즘을 재조명한다. 주요 결과로써, 네트워크 내 노드수 및 네트워크 위상과 같은 다양한 네트워크 변수가 주어질 때, 타이밍 동기화 시간을 최소화 하는 방식에서 결합 함수 및 검파 임계값을 최적 설계함으로써 새로운 동기 코드 검파기를 소개한다. 실제적인 네트워크 환경에서 동기 상태로의 수렴을 보이기 위해 컴퓨터 모의실험을 수행한다.