moth-flameoptimization (MFO) technique has recently been explored to develop a novel algorithm for distributed optimization and control. In this paper, the MFO-based design of blade pitch controllers (BPCs) is propos...
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moth-flameoptimization (MFO) technique has recently been explored to develop a novel algorithm for distributed optimization and control. In this paper, the MFO-based design of blade pitch controllers (BPCs) is proposed for wind energy conversion system (WECS) to enhance the damping of oscillations in the output power and voltage. The simple Proportional-Integral-Differential (PID) is used to realize the advantage of the proposed hybrid referential integrity MFO technique. The proposed blade pitch controllers are termed as BPC-PID (MFO). Single wind turbine system, equipped with BPC-PID (MFO), is considered to accomplish this study. The suggested WECS model considers small as well as large scale uncertainties. MFO is utilized to search for optimal controller parameters by minimizing a candidate time-domain based objective function. The performance of the proposed controller has been compared to those of the conventional PID controller based on Zeigler Nichols and simplex algorithm and the PID controller optimized by genetic algorithms (GA), to demonstrate the superior efficiency of the MFO-based BPC-PID. Simulation results emphasis on the better performance of the proposed BPC-PID (MFO) compared to conventional and GA-based BPC-PID controllers over a wide range of operating conditions and control system parameters uncertainties.
Vibration-based structural damage detection through optimizationalgorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well ...
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Vibration-based structural damage detection through optimizationalgorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimizationalgorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using moth-flameoptimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimizationalgorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.
In this paper, a pattern synthesis based on a multiobjective optimizationalgorithm is proposed for the generation of a reconfigurable pencil/flat top dual-beam planar antenna array built using isotropic antenna eleme...
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In this paper, a pattern synthesis based on a multiobjective optimizationalgorithm is proposed for the generation of a reconfigurable pencil/flat top dual-beam planar antenna array built using isotropic antenna elements in selected phi cuts. These beams claim the same amplitude excitations and differ from each other in phase excitations. Zero-phase excitations are used in pencil beam and these phases are updated with optimum phases for the flat top beam. All the excitations are obtained using moth-flame optimization algorithm. With the support of the fitness functions, care is taken to control the expected values of the radiation pattern parameters to remain under certain fixed limit. In addition, synthesis is also done for the provision of a null in a particular direction for rejection of interference in the pencil beam in two different phi cuts. To suppress the mutual coupling effects, dynamic range ratio is kept under a threshold limit. Simulation results show the effectiveness of this proposed synthesis for phi cut planes. This algorithm is compared and proved to be better in many aspects over the standard meta-heuristic algorithms like Artificial Bee Colony and Imperialist Competitive algorithms in terms of performance parameters.
Piano key weirs (PKWs) are acquired and developed for free surface control structures which improve their performance by increasing the storage capacity and flood evacuation. In this study, the potential combinations ...
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Piano key weirs (PKWs) are acquired and developed for free surface control structures which improve their performance by increasing the storage capacity and flood evacuation. In this study, the potential combinations of two popular artificial intelligence data-driven models (Al-DDMs) of multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS) with four meta-heuristic optimizationalgorithms (particle swarm optimization, genetic algorithm, firefly algorithm & moth-flameoptimization) are assessed for predicting the PKW's flow rate. Comparing the outcomes of the ten standard and hybrid Al-DDMs with three empirical relations based on several statistics and diagnostic analysis (such as the Taylor diagram) for estimating the flow rate shows that the AI-DDMs can predict the passing flow more accurately. In addition, the particle swarm optimization and firefly algorithm meta-heuristic algorithms improve the performance of ANFIS and MLPNN, respectively. The Mann-Whitney test for investigating the differences between two independent applied models indicates a significant difference between the Al-DDMs and two of the empirical relations at the 95% confidence level.
moth-flameoptimization (MFO) algorithm is a widely used nature-inspired optimizationalgorithm. However, for some complex optimization problems, such as high dimensional and multimodal problems, the MFO may fall into...
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moth-flameoptimization (MFO) algorithm is a widely used nature-inspired optimizationalgorithm. However, for some complex optimization problems, such as high dimensional and multimodal problems, the MFO may fall into the local optimal solution. Hence, in this paper an ameliorated moth-flameoptimization (AMFO) algorithm is presented to improve the solution quality and global optimization capability. The key features of the proposed algorithm are the Gaussian mutation produce flames and the modified position updating mechanism of moths, which can improve the ability of MFO to jump out of local optimum solutions. In addition, opposition-based learning is adopted to initialize the population. The AMFO algorithm is compared with 9 state-of-the-art algorithms (such as Levy moth-flameoptimization (LMFO), Grey Wolf optimization (GWO), Sine Cosine algorithm (SCA), Heterogeneous Comprehensive Learning Particle Swarm optimization (HCLPSO)) on 23 classical benchmark functions. The comparative results show that the AMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Furthermore, the AMFO is adopted to optimize the parameters of fast learning network (FLN) to build the prediction model of silicon content in liquid iron for blast furnace, and simulation experiment results from field data show that the root mean square error of the AMFO-FLN model is 0.0542, hit ratio is 91 and the relative error is relatively stable, the main fluctuation is between-0.1 and 0.1;compared with other ten silicon content in liquid iron models, the AMFO-FLN model has better predictive performance. (C) 2020 The Author(s). Published by Elsevier B.V.
Power system stabilizers (PSS) are utilized to mitigate power system oscillations by excitation control. This paper proposes a PSS design based on a new population-based algorithm named moth-flameoptimization (MFO) a...
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ISBN:
(纸本)9781538623176
Power system stabilizers (PSS) are utilized to mitigate power system oscillations by excitation control. This paper proposes a PSS design based on a new population-based algorithm named moth-flameoptimization (MFO) algorithm in a multi-machine power system. A conventional speed-based lead-lag PSS is used for design purposes. The parameters of the sine-cosine algorithm based PSS are tuned using MFO to shift the weakly damped electromechanical mode eigenvalues towards the left half of S plane. Results so obtained has been extensively compared with those obtained by Bacteria Foraging (BF) and firefly algorithm (FA) which show the effectiveness of MFO in enhancing overall system stability over an extensive range of loading conditions and fault scenario.
Phenanthrene, a PAH with three fused benzene rings, is usually used as a model for the study on PAHs. During 4 days, 166 male mice were equally and randomly divided into two groups. One group was given vehicle-corn oi...
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Phenanthrene, a PAH with three fused benzene rings, is usually used as a model for the study on PAHs. During 4 days, 166 male mice were equally and randomly divided into two groups. One group was given vehicle-corn oil by oral gavage, the other was given phenanthrene at a dose of 450 milligrams per kilogram per day. In this study, in order to predict mice's phenanthrene poisoning by virtue of blood analysis indices, a new machine learning approach was put forward, which was based on an improved binary mothflame optimizer combined with extreme learning machine. The results of the experiment have manifested that the blood analysis indices of the control and phenanthrene groups were significantly different (p < 0.5). The most important correlated indices including serum alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), plateletcrit (PCT) and red blood cell distribution width-standard deviation (RDW-SD) were screened through feature selection. The classification results demonstrated that the proposed method can achieve 93.38% accuracy and 98.33% specificity. Promisingly, there is a new and accurate way to detect the status of phenanthrene poisoning expectably.
This paper proposes an effective non-dominated moth-flame optimization algorithm (NS-MFO) method for solving multi-objective problems. Most of the multi-objective optimizationalgorithms use different search technique...
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This paper proposes an effective non-dominated moth-flame optimization algorithm (NS-MFO) method for solving multi-objective problems. Most of the multi-objective optimizationalgorithms use different search techniques inspired by different optimization techniques such as genetic algorithms, differential evolutions, particle swarm optimization, cuckoo search etc., but search techniques of recently developed metaheuristics have hardly been investigated. Non-dominated moth-flame optimization algorithm (NSMFO) is based on the search technique of moth-flame optimization algorithm (MFO) algorithm and utilizes the elitist non-dominated sorting and crowding distance approach for obtaining different non domination levels and to preserve the diversity among the optimal set of solutions respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems and multi-objective engineering design problems with distinctive features. It is shown in this paper that this method effectively generates the Pareto front and also, this method is easy to implement and algorithmically simple. (C) 2017 Elsevier Ltd. All rights reserved.
In this paper, a newly surfaced nature-inspired optimization technique called moth-flameoptimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspire...
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In this paper, a newly surfaced nature-inspired optimization technique called moth-flameoptimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature. (C) 2017 Elsevier B.V. All rights reserved.
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