This paper deals with a machine repair problem in which M identical operating machines are maintained by one server (repairman). The server is subject to breakdown and operates a threshold recovery policy. The thresho...
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This paper deals with a machine repair problem in which M identical operating machines are maintained by one server (repairman). The server is subject to breakdown and operates a threshold recovery policy. The threshold recovery policy means that when the server breaks down, the repair can be performed until q (1 <= q <= M) or more machines appear. The failure and service times of the machines, and the repair times of the server, are all assumed to be exponentially distributed. A cost model is developed to determine the optimal threshold value, optimal service rate and optimal repair rate. We then apply the particle swarm optimization algorithm to solve this optimization problem. Finally, some numerical examples are provided for illustrative purposes.
This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR mo...
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This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particleswarmoptimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study. (C) 2014 Elsevier Inc. All rights reserved.
Due to the crucial importance of the FACTS based damping controllers in mitigation the deteriorative impacts of the power system low frequency oscillations, particularly the inter-area modes, improving the system stab...
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Due to the crucial importance of the FACTS based damping controllers in mitigation the deteriorative impacts of the power system low frequency oscillations, particularly the inter-area modes, improving the system stability margins seems indispensible. This paper proposes an optimization approach to effectively carry out the multi-machine based stabilization function of the Gate-Controlled Series Capacitor (GCSC) in providing a robust damping to the power system low frequency oscillations. It is aimed to provide a reliable damping framework by means of an optimized GCSC based supplementary damping controller. Thus, to attain the most efficient set of the damping controller parameters, particle swarm optimization algorithm as a proficient optimum hunter is employed to explore for the global stabilization solution in accordance to a vast range of power system operating conditions. Moreover, as a weighty assessment, the eigenvalue analysis is taken into account as the cornerstone of the performed studies in order to investigate the damping methodology in which the unstable or lightly damped inter-area modes are scheduled to effectively shift to some predominant stability zones in the s-plane. Meanwhile, derived results through the nonlinear time domain simulation as well as two dynamic performance evaluators manifestly demonstrate the impressiveness and verify the robustness of the proposed GCSC based damping scheme in enhancing the power system stability, especially regarding to the inter-area modes. (C) 2013 Elsevier Ltd. All rights reserved.
Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation bec...
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Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarmparticle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance and the sensitivity of the proposed method. The efficiency of the proposed method was analyzed by using different noise levels and sensor numbers. Results show that the proposed method is effective and can be applied in many types of large structures.
In this paper, a novel grey prediction model is proposed to enhance the performance of prediction for the amount of fixed-line and cellular phone subscribers in Japan. The cubic spline function is first integrated int...
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In this paper, a novel grey prediction model is proposed to enhance the performance of prediction for the amount of fixed-line and cellular phone subscribers in Japan. The cubic spline function is first integrated into grey prediction model to enhance its prediction capability. Then the particleswarmoptimization (PSO) algorithm is applied, so that the prediction performance can be improved further. The prediction results using proposed models are very satisfactory. (C) 2013 Elsevier Inc. All rights reserved.
This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer,...
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This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer, are employed to testify the proposed algorithm. The computational results reveal that aiNetK is superior to particle swam optimization and artificial immune system-related methods.
The electromagnetic shunt damping absorber (EMSDA) is developed based on electromagnetic shunt damping mechanism. The governing equation of planar vibration system equipped with the EMSDA is derived. An optimization m...
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The electromagnetic shunt damping absorber (EMSDA) is developed based on electromagnetic shunt damping mechanism. The governing equation of planar vibration system equipped with the EMSDA is derived. An optimization method is presented to determine the main working parameters of EMSDA on the basis of the built theoretical model. The objective function minimizing the response variance of system under white noise excitation is formulated. The particle swarm optimization algorithm is employed in optimization. The simulated and experimental studies on vibration control by use of EMSDA are conducted. The results show that the electromagnetic shunt damping absorber can attenuate significantly the structural vibration.
The aim of this study is to design nonlinear robust controllers for multimachine power systems. A technique for the optimal tuning of Power System Stabilizer (PSS) by integrating the particleswarmoptimization (PSO) ...
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In this study, a new discrete parallel particleswarmoptimization (PSO) method is presented for long term Transmission Network Expansion Planning (TNEP) with security constraints. The procedure includes obtaining the...
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In order to achieve precise collision avoidance for large ships, a novel intelligent collision avoidance control approach is presented in this paper. To obtain the precise collision avoidance information capability, a...
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ISBN:
(纸本)9781479931972
In order to achieve precise collision avoidance for large ships, a novel intelligent collision avoidance control approach is presented in this paper. To obtain the precise collision avoidance information capability, a fuzzy set interpretation is developed to handle imprecise information. This allows the introduction of an innovative self-training optimizing search method. The optimizing process is based on the particle swarm optimization algorithm and off-line training data that is obtained from trial manoeuvres based on computer simulations. The resulting controller can decrease the ship operators' burden to deal with bridge data and help them to make timely and precise collision avoidance decision. The results show that the designed intelligent controller performed well to implement the optimizing control of ship collision avoidance.
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