To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimizationalgorithms. The moth-flameoptimization（MFO） algorithm is an...
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To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimizationalgorithms. The moth-flameoptimization（MFO） algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO,an ameliorated moth-flame optimization algorithm（A-MFO） that combines the crisscross optimizationalgorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.
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.
moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on the navigation routine of moths in the environment known as transverse orientation. For some complex optimization tasks, especia...
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moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on the navigation routine of moths in the environment known as transverse orientation. For some complex optimization tasks, especially high dimensional and multimodal problems, the conventional MFO may face problems in the convergence trends or be trapped into the local and deceptive optima. Therefore, in this study, two strategies have been introduced into the conventional MFO to get a more stable sense of balance between the exploration and exploitation propensities. First, Gaussian mutation is employed to increase the population diversity of MFO. Then, a chaotic local search is applied to the flame updating process of MFO for better exploiting the locality of the solutions. The proposed CLSGMFO approach was compared against a wide range of well-known classical metaheuristic algorithms (MAs) and various advanced MAs using 23 classical benchmark functions. It was shown that the designed CLSGMFO can outperform most of the popular MAs in terms of solution quality and convergence speed. Moreover, based on CLSGMFO, a hybrid kernel extreme learning machine model, which is called CLSGMFO-KELM, is established to deal with financial stress prediction scenarios. To investigate the effectiveness of the CLSGMFO-KELM model, the proposed hybrid system was tested on two widely used financial datasets and compared against a broad array of popular classifiers. The results demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance. Accordingly, the proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction. (C) 2019 Elsevier Ltd. All rights reserved.
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.
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.
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.
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.
This work presents a novel methodology for variable speed high power Transfer Capability of a self-excited induction generator (SEIG). The proposed methodology is based on the selection of a suitable firing angle of F...
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This work presents a novel methodology for variable speed high power Transfer Capability of a self-excited induction generator (SEIG). The proposed methodology is based on the selection of a suitable firing angle of Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR) for achieving constant rated stator current. WDSEIG would produce a variable speed high power without overheating under variable wind speed and connected load. The analytical approach for the proposed methodology has been implemented to predict the optimal operating firing angle of FC-TCR for full load stator current achievement within the allowed operating range of load and prime mover speed. Also, Soft Computing (SC) techniques have been implemented based on Harmony Search algorithm (HSA), Flower Pollination algorithm (FPA), and moth-flameoptimization (MFO) algorithm to achieve the proposed methodology. A comparison between different SC techniques, analytical approach and experimental work are given and evaluated to verify SC techniques accuracy. This evaluation study can be useful in specifying the appropriateness of the SC techniques for High Power Transfer Capability for a SEIG.
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