The moth-flame optimization algorithm is a new bionic swarm intelligence *** the moth's behavior has a large number of random states and need to repeatedly test in the algorithm,which takes *** this paper,the basi...
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The moth-flame optimization algorithm is a new bionic swarm intelligence *** the moth's behavior has a large number of random states and need to repeatedly test in the algorithm,which takes *** this paper,the basic principle of the moth-flamealgorithm is analyzed deeply,and proposed a modified moth-flame *** core is to improve and optimize the adaptive mechanism for the number of flames,and to change the flame adaptive mechanism along the straight line to decrease along the curve,so as to improve the convergence speed of the adaptive flame number;Given the ability of "study" to the moths when moths update position,that all moths update the position with reference to the best flame,so as to improve the search *** testing 8 classical test functions and 1 engineering example,it is proved that the modified moth-flamealgorithm has the advantages of faster convergence speed,higher search precision and avoiding local *** significant computational efficiency and precision of the improved moth-flamealgorithm can be used to improve the ability to solve practical engineering problems.
optimization problem is widely used in production management, military command and scientific experiments and other fields, moth-flame optimization algorithm as a new swarm intelligence optimizationalgorithm, has the...
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ISBN:
(纸本)9783031138324;9783031138317
optimization problem is widely used in production management, military command and scientific experiments and other fields, moth-flame optimization algorithm as a new swarm intelligence optimizationalgorithm, has the advantages of fast convergence, simple structure, strong robustness, memory mechanism, it is also one of the focus of scholars. K-means clustering is the most famous partition clustering algorithm. Given a set of data points and the required number of clustering k, k is specified by the user, and the K-means algorithm repeatedly divides the data into K clusters according to a certain distance function. This article first on moth-flame optimization algorithm (MFO) the existence of complex or slightly larger scale function to solve the problems of slow convergence speed, put forward by the flame number greater than the number of moths reference grey wolf optimizer (GWO) comes first algorithm do rectilinear flight, later periods the scaling factor are introduced to improve moth-flame optimization algorithm, in order to realize broaden the moths search area, improve the ability of global optimization and convergence rate of the target. Through experiments to verify the feasibility of the improved moth-flame optimization algorithm (IMFO), the convergence speed is significantly higher than MFO algorithm, and the solution accuracy is also greatly improved. Then the algorithm is used to guide the clustering center of k-means clustering algorithm to improve the clustering accuracy. The three algorithms of K-means, MFO Fusion K-means (MFO-KM) and IMFO Fusion K-means (IMFO-KM) algorithms were compared in the international standard data set Iris, Seeds and Wine Quality. The results showed that: IMFO-KM algorithm has the best performance improvement in Wine Quality data set, with the accuracy improved by 3.82%-6.37% compared with other algorithms, the class average distance G reduced by 7.18%-13.58%, and the standardized mutual information improved by 14.17%.
This paper considers two stochastic diffusion processes associated with a general growth curve that includes a wide family of growth phenomena. The resulting processes are lognormal and Gaussian, and for them inferenc...
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This paper considers two stochastic diffusion processes associated with a general growth curve that includes a wide family of growth phenomena. The resulting processes are lognormal and Gaussian, and for them inference is addressed by means of the maximum likelihood method. The complexity of the resulting system of equations requires the use of metaheuristic techniques. The limitation of the parameter space, typically required by all metaheuristic techniques, is also provided by means of a suitable strategy. Several simulation studies are performed to evaluate to goodness of the proposed methodology, and an application to real data is described.
Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical m...
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Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.
The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the develop...
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The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm.
From the perspective of bionics of biological structure, this paper proposes a new reservoir topology structure with an a -helix form of the secondary protein, named S -ESN. This network model has some advantages comp...
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From the perspective of bionics of biological structure, this paper proposes a new reservoir topology structure with an a -helix form of the secondary protein, named S -ESN. This network model has some advantages compared with the standard leaky -echo state network (Leaky -ESN) model. Because the neurons in the traditional reservoir are randomly and sparsely connected, the stability of the echo state network (ESN) will be reduced, and the prediction accuracy will also be decreased. The S -ESN model proposed greatly improves the internal stability of the reservoir, the dynamic activity of neurons and the prediction accuracy of the ESN. At the same time, the improved moth -flameoptimizationalgorithm (MFO) with the probability of jump disturbance is used to optimize the three parameters: the leakage rate (a), the spectral radius (p), and the input scaling factor (s'"), which can further improve the stability and predictability of the S -ESN. In order to verify the performance of S -ESN, three virtual time series Sin time series with low frequency, Sin time series with high frequency, Mackey -Glass time series (MG) and one practical Sunspot are selected as experimental data. The experimental results show that the S -ESN model has better prediction accuracy.
Evolutionary population-based methods have found their applications in dealing with many real-world simulation experiments and mathematical modelling problems. The moth-flameoptimization (MFO) algorithm is one of the...
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Evolutionary population-based methods have found their applications in dealing with many real-world simulation experiments and mathematical modelling problems. The moth-flameoptimization (MFO) algorithm is one of the swarm intelligence algorithms and it can be used with constrained and unknown search spaces. However, there are still some defects in its performance, such as low solution accuracy, slow convergence, and insufficient exploration capability. This study improves the basic MFO algorithm from the perspective of improving exploration capability and proposes a hybrid swarm-based algorithm called SMFO. The essential notion is to further explore and scan the feature space with taking advantages of the sine cosine strategy. We methodically investigated the efficacy, solutions, and optimization compensations of the developed SMFO using more than a few demonstrative benchmark tests, together with unimodal, multimodal, hybrid and composition tasks, and two widely applied engineering test problems. The simulations point towards this fact that the diversification and intensification inclinations of the original MFO and its convergence traits are fortunately upgraded. The findings and remarks show that the suggested SMFO is a favourable algorithm and it can show superior efficacy compared to other techniques. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
The Load Disaggregation (LD) is an optimizing problem. The actual operation states of the appliances would not serve as an optimal solution for a single-objective function, due to various noises as well as frequency i...
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The Load Disaggregation (LD) is an optimizing problem. The actual operation states of the appliances would not serve as an optimal solution for a single-objective function, due to various noises as well as frequency interferences from the adjacent systems. In this paper, the LD model with a multi-objective function combines the appliance features at both macro and micro levels. This model contributes a good representation of the appliances from several viewpoints. Recognizing numerous appliances is carried out through five objective functions using apparent, active, and reactive powers, currents, and harmonics as the loads characteristic. The suggested problem is solved utilizing moth-flameoptimization (MFO) algorithm with several objectives for LD. Besides, it prevents tuning the weighted parameters and does not ignore the conflict among the objectives. In addition, the Factorial Hidden Markov Model (FHMM) is used to define the allowable modes of the appliances for the next second. It could be resorted to an objective-rank project to cope with the restraint on the number of appliances functioning concurrently. The efficiency of the suggested method for LD is shown by experimental outcomes and is compared with other methods. The results of various combinations of appliances features are evaluated by various evaluation metrics. It is presented that in more features, the results are more accurate. The results show that the accuracy of the proposed method is at least 20 % more than others. (C) 2021 The Authors. Published by Elsevier Ltd.
The hysteresis existing in the piezoelectric positioning stage seriously affects the performance of the positioning stage, and even causes the instability of the system. However, due to the complexity of hysteresis, h...
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ISBN:
(纸本)9781728176871
The hysteresis existing in the piezoelectric positioning stage seriously affects the performance of the positioning stage, and even causes the instability of the system. However, due to the complexity of hysteresis, hysteresis modeling and parameter identification are still a challenging task. According to the hysteresis characteristic of the piezoelectric positioning stage, a hysteresis parameter identification method based on the improved moth-flameoptimization (IMFO) algorithm is proposed. First, the diversity of IMFO is enhanced by differential evolution (DE) algorithm, then the subpopulation update strategy is used to balance the exploration and exploitation capability of the algorithm, so as to effectively improve the performance of the identification algorithm. Finally, for the identification problem of the enhanced Prandll-Ishlinskii (PI) model in the piezoelectric positioning stage, the proposed identification algorithm is compared with five stochastic optimizationalgorithms. The experimental results show that the proposed algorithm has higher accuracy, which also demonstrates the effectiveness of the algorithm.
In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode con...
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In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode control (SMC) into GCCD controller. Firstly, the mathematical model of MMC converter is established, and the sliding mode controller is designed based on the SMC principle. Then, aiming at the problems of complex controller structure and difficult parameter tuning in multiple modes of the GCCD, this paper proposes a controller parameter optimization method based on improved Month flameoptimization (IMFO) algorithm. This method improves the MFO algorithm by introducing good point set (GPS) initialization and Levy flight strategy, which accelerates the convergence speed of the algorithm while avoiding falling into local optimization, and realizes the optimization of converter controller parameters. Under a variety of standard test functions, the advantages of the proposed IMFO algorithm are verified by comparing it with the traditional algorithm. Finally, in order to realize the automatic tuning of control parameters, the Python-PSCAD joint simulation method is studied and implemented. Taking the comprehensive integral of time and absolute error (CITAE) index as the objective function, the parameters of the sliding mode controller are optimized. The simulation results show that the controller parameters optimized by the IMFO algorithm can make the GCCD have better dynamic performance.
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