The behavior decision-making algorithm plays an important role in ensuring the safe driving of autonomous vehicles. However, existing behavior decision-making methods lack the capability to cope with future motion unc...
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The behavior decision-making algorithm plays an important role in ensuring the safe driving of autonomous vehicles. However, existing behavior decision-making methods lack the capability to cope with future motion uncertainty in traffic, because the historical state of vehicles are not considered. This article proposes a novel driving behavior decision-making method EnMFO-ImGRU based on Gated Recurrent Unit (GRU) and moth-flame optimization algorithm (MFO). Four improvements are proposed in EnMFO-ImGRU. First, to consider the driving information of the vehicles on the road, ImGRU is designed based on a double-layer GRU. Second, to promote decisions accuracy, Support Vector Machine (SVM), which has good performance in classification problems, replaces the softmax classifier to train the output of the ImGRU. Third, to promote the classification capability of SVM, MFO is introduced to optimize the key parameters that affect the performance of SVM. Finally, to promote the optimization capability of MFO, we propose the Enhanced moth-flame optimization algorithm (EnMFO). A new position updating method is proposed in EnMFO. The experimental results on the NGSIM dataset show that EnMFO-ImGRU brings higher accuracy than existing methods for the behavior decision-making results of autonomous vehicles.
The article presents the possibility of using the moth-flameoptimization (MFO) algorithm for Abrasive Water Jet machining (AWJ) of structural steel materials. In order to carry out the optimization, an original progr...
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The article presents the possibility of using the moth-flameoptimization (MFO) algorithm for Abrasive Water Jet machining (AWJ) of structural steel materials. In order to carry out the optimization, an original program was written in Python programming language. In turn, for this optimization process the objective function was determined using the Response Surface Methodology (RSM). Then, a set of control parameters was determined at which the value of the objective function reaches an extreme value. The optimal value calculated based on the moth-flame optimization algorithm was compared with the value of the best effect determined experimentally.
To handle the path planning problem of unmanned aerial vehicles(UAV) meeting numerous obstacles in complicated environments, an improved moth-flameoptimization(MFO) algorithm with periodic and Gaussian mutations(PGMF...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
To handle the path planning problem of unmanned aerial vehicles(UAV) meeting numerous obstacles in complicated environments, an improved moth-flameoptimization(MFO) algorithm with periodic and Gaussian mutations(PGMFO) is proposed. For PGMFO, firstly, moths are able to jump out of local optima and better their position by means of periodic ***, the Gaussian mutation is used to select the optimal flame in each iteration can improve the development capability of MFO. Finally, PGMFO is contrasted with other 9 optimizationalgorithms using 23 test problems. According to the findings,PGMFO performs faster and more accurately during convergence in the majority of optimization issues. In addition, in the generation of benchmark scenes in real digital elevation model maps, PGMFO is used to solve the optimization problem of feasible and safe operation requirements and constraints for UAVs. The results show that the validity of the PGMFO.
moth-flame optimization algorithms are widely employed to solve optimization problems and achieve good performance. However, the algorithms suffer the shortcoming of prematurity because of the early gathering of flame...
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ISBN:
(纸本)9781665435543
moth-flame optimization algorithms are widely employed to solve optimization problems and achieve good performance. However, the algorithms suffer the shortcoming of prematurity because of the early gathering of flames. To solve this problem, the flame fusion mechanism is integrated to improve the exploratory behavior of the moth-flame optimization algorithm. The flame fusion mechanism provides a new way to evaluate the state of flame aggregation based on the distribution of flames and moths. When the concentration of flames is higher than the fusion threshold, the better flame will fuse other flames. And the fused flames will be regenerated to enhance the exploration behavior of the algorithm. At the same time, the fusion rate that determines the probability of flame fusion is introduced. The fusion rate changes during iteration to balance the exploration and exploitation behaviors of the algorithm. The improved moth-flameoptimization is validated by ten benchmark functions. The results show that the optimization ability of the improved moth-flame optimization algorithm is improved, and the stability is higher than compared algorithms as well.
moth-flameoptimization (MFO) algorithm, which is inspired by the navigation method of moths, is a nature-inspired optimizationalgorithm. The MFO is easy to implement and has been used to solve many real-world optimi...
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moth-flameoptimization (MFO) algorithm, which is inspired by the navigation method of moths, is a nature-inspired optimizationalgorithm. The MFO is easy to implement and has been used to solve many real-world optimization problems. However, the MFO cannot balance exploration and exploitation well, and the information exchange between individuals is limited, especially in solving some complex numerical problems. To overcome these disadvantages of the MFO in solving the numerical optimization problems, a covariance-based moth-flame optimization algorithm with Cauchy mutation (CCMFO) is proposed in this paper. In the CCMFO, the concept of covariance is used to transform the individuals of the moths and flames from the original space to the eigenspace and update the positions of moths, which can better improve the information exchange ability of the flames and moths in the eigenspace. In addition, Cauchy mutation is utilized to improve the exploration. And the CCMFO is compared with the other 22 algorithms on CEC 2020 test suite. The test results show that the CCMFO is better than other population-based optimizationalgorithms and MFO variants in search performance, while its performance is statistically similar to CEC competition algorithms. Furthermore, the CCMFO is compared with the other 12 algorithms on CEC 2020 real -world constrained optimization problems, and the results show that the CCMFO can effectively solve real-world constrained optimization problems. Finally, the CCMFO is used to optimize the tracking controller parameters of continuous casting mold vibration displacement. The experimental results based on the experimental platform show that the CCMFO can effectively reduce the difficulty of parameter selection and improve the tracking accuracy. (c) 2022 Elsevier B.V. All rights reserved.
moth-flameoptimization (MFO) is widely utilized to solve optimization problems in different fields since it has a simple structure and easy implementation. However, MFO cannot effectively balance exploration and expl...
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moth-flameoptimization (MFO) is widely utilized to solve optimization problems in different fields since it has a simple structure and easy implementation. However, MFO cannot effectively balance exploration and exploitation and often suffers from the lack of population diversity in the search process, especially in solving some complex engineering optimization problems. To overcome the above problems, in this paper, a multiswarm improved moth-flamealgorithm (MIMFO) is proposed. In MIMFO, firstly, the population is grouped and dynamically reorganized through chaotic grouping mechanism and dynamic regrouping mechanism, which can improve the grouping quality and diversity of the population. Secondly, spiral search and linear search are carried out for the two sub-swarms to improve the search efficiency and balance exploration and exploitation. In addition, Gaussian mutation is used to generate flame, which can accelerate convergence and enhance the exploration. The MIMFO is verified on 13 benchmark problems with 30, 500, 1000, 2000 dimensions and CEC 2014 test functions. The results show that the MIMFO is superior to other swarm intelligence algorithms and MFO variants in finding the global optimum and convergence performance. Finally, MIMFO is used to solve 57 engineering constraint optimization problems, and the results show that MIMFO can solve real-world engineering problems.
moth-flameoptimization (MFO) algorithm is a new population-based meta-heuristic algorithm for solving global optimization problems. flames generation and spiral search are two key components that affect the performan...
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moth-flameoptimization (MFO) algorithm is a new population-based meta-heuristic algorithm for solving global optimization problems. flames generation and spiral search are two key components that affect the performance of MFO. To improve the diversity of flames and the searching ability of moths, an improved moth-flameoptimization (IMFO) algorithm is proposed. The main features of the IMFO are: the flames are generated by orthogonal opposition-based learning (OOBL);the modified position updating mechanism of moths with linear search and mutation operator. To evaluate the performance of IMFO, the IMFO algorithm is compared with other 20 algorithms on 23 benchmark functions and IEEE (Institute of Electrical and Electronics Engineers) CEC (Congress on Evolutionary Computation) 2014 benchmark test set. The comparative results show that the IMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Moreover, the IMFO is also used to solve three engineering optimization problems, and it is compared with other well-known algorithms. The comparison results show that the IMFO algorithm can improve the global search ability of MFO and effectively solve the practical engineering optimization problems.
In order to present an integrated approach to optimal automobile component design, this research is focused on a shape optimization problem of a bracket using moth-flame optimization algorithm (MFO) and response surfa...
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In order to present an integrated approach to optimal automobile component design, this research is focused on a shape optimization problem of a bracket using moth-flame optimization algorithm (MFO) and response surface methodology. First, the multiple disc clutch brake problem is optimized using the MFO. Finally, the design problem is posed for shape optimization of the bracket with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis while the response surface method is used to obtain the equations for objective and constraint functions. Weight reduction of the bracket is 45.2 % using the MFO. The results show the ability of the MFO to optimize automobile components in the industry.
With the expansion of grid-connected solar power generation, the variability of photovoltaic power generation has become increasingly pronounced. Accurate photovoltaic output prediction is necessary to ensure power sy...
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With the expansion of grid-connected solar power generation, the variability of photovoltaic power generation has become increasingly pronounced. Accurate photovoltaic output prediction is necessary to ensure power system stability. In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. The former balances the search and mining capabilities at the population location search equation, and the latter helps to increase the diversity of the masses and to void avoid entrapment into local optima. Various meteorological conditions affecting the photovoltaic power generation are discussed and the experimental input data is optimized by grey relational analysis. The results using multiple test functions and the real data of photovoltaic power station in Australia have verified that the proposed model has better optimization performance compared with other models. The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid, and maintains the system reliability. (C) 2020 Elsevier Ltd. All rights reserved.
In this paper, a discrete moth-flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth-flame opt...
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In this paper, a discrete moth-flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth-flameoptimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity.
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