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A Novel Gated Recurrent Unit Network Based on SVM and moth-flame optimization algorithm for Behavior Decision-Making of Autonomous Vehicles

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IEEE ACCESS 2021年 9卷 20410-20422页

作者： Yin, Taiqiao Li, Ying Fan, Jiahao Wang, Tan Shi, Yunxia Jilin Univ Coll Software Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Space Technol Jilin Co Ltd Jilin 132013 Jilin Peoples R China

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.

关键词： Feature extraction Mathematical model Decision making Support vector machines optimization Logic gates Licenses Autonomous vehicles behavior decision-making gated recurrent unit support vector machine moth-flame optimization algorithm

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The moth-flame optimization algorithm of Abrasive Water Jet Cutting Process

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Procedia Computer Science 2024年 246卷 2912-2921页

作者： Elżbieta Kawecka Piotr Puzio The Jacob of Paradies University Faculty of Technology Teatralna 25 66-400 Gorzów Wielkopolski Poland

The article presents the possibility of using the moth-flame optimization (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.

关键词： optimization algorithms meta-heuristic algorithms moth-flame optimization algorithm Abrasive Water Jet (AWJ)

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An improved moth-flame optimization algorithm with periodic mutation and Gaussian mutation for safety-enhanced UAV path planning

An improved moth-flame optimization algorithm with periodic ...

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第43届中国控制会议

作者： Xiaodong Zhao Zhiqiang Hu Xiaoqian Li Xianliang Zhang School of Mathematics and Statistics Taishan University

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-flame optimization(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 optimization algorithms 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.

关键词： moth-flame optimization algorithm periodic mutation Gaussian mutation UAV path planning

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An improved moth-flame optimization algorithm based on fusion mechanism 47

An improved moth-flame optimization algorithm based on fusio...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Jiang, Luchao Hao, Kuangrong Tang, Xue-song Wang, Tong Liu, Xiaoyan Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

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-flame optimization 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.

关键词： moth-flame optimization algorithm optimization problem swarm intelligence fusion mechanism

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A covariance-based moth-flame optimization algorithm with Cauchy mutation for solving numerical optimization problems

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APPLIED SOFT COMPUTING 2022年第0期119卷 108538-108538页

作者： Zhao, Xiaodong Fang, Yiming Liu, Le Xu, Miao Li, Qiang Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao 066004 Peoples R China Hebei Normal Univ Coll Engn Shijiazhuang 050024 Peoples R China

moth-flame optimization (MFO) algorithm, which is inspired by the navigation method of moths, is a nature-inspired optimization algorithm. 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 optimization algorithms 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.

关键词： moth-flame optimization algorithm Covariance matrix Cauchy mutation Numerical optimization problems Engineering optimization problems

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Multi-swarm improved moth-flame optimization algorithm with chaotic grouping and Gaussian mutation for solving engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 204卷

作者： Zhao, Xiaodong Fang, Yiming Ma, Shuidong Liu, Zhendong Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China North China Univ Sci & Technol Coll Continuing Educ Tangshan 063210 Peoples R China

moth-flame optimization (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-flame algorithm (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.

关键词： moth-flame optimization algorithm Multi-swarm Gaussian mutation Linear search Engineering optimization problems

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An improved moth-flame optimization algorithm with orthogonal opposition-based learning and modified position updating mechanism of moths for global optimization problems

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APPLIED INTELLIGENCE 2020年第12期50卷 4434-4458页

作者： Zhao, Xiaodong Fang, Yiming Liu, Le Li, Jianxiong Xu, Miao Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Minist Educ Intelligent Control Syst & Intelligen Res Ctr Qinhuangdao 066004 Hebei Peoples R China

moth-flame optimization (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-flame optimization (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.

关键词： moth-flame optimization algorithm Orthogonal experiment design Opposition-based learning Mutation operator Engineering optimization problems

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Optimal design of automobile structures using moth-flame optimization algorithm and response surface methodology

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MATERIALS TESTING 2020年第4期62卷 371-377页

作者： Yildiz, Betul Sultan YOK UAK Bursa Turkey Bursa Uludag Univ Bursa Bursa Turkey Bursa Tech Univ Mech Engn Bursa Turkey

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.

关键词： moth-flame optimization algorithm response surface bracket multiple disc clutch brake shape optimization

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An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation

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JOURNAL OF CLEANER PRODUCTION 2020年 253卷 119966-119966页

作者： Lin, Guo-Qian Li, Ling-Ling Tseng, Ming-Lang Liu, Han-Min Yuan, Dong-Dong Tan, Raymond R. Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia Fac Econ & Management Bangi Malaysia ZhangJiakou Wind Photovolta & Energy Storage Demo Zhangjiakou 075061 Peoples R China De La Salle Univ Gokongwei Coll Engn Chem Engn Dept Manila Philippines

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.

关键词： Support vector machine moth-flame optimization algorithm Power prediction PV power generation Particle swarm optimization

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DMFO-CD: A Discrete moth-flame optimization algorithm for Community Detection

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algorithmS 2021年第11期14卷 314页

作者： Nadimi-Shahraki, Mohammad H. Moeini, Ebrahim Taghian, Shokooh Mirjalili, Seyedali Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad *** Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad *** Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

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 optimization 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.

关键词： community detection complex network optimization metaheuristic algorithms swarm intelligence algorithms moth-flame optimization algorithm

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