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moth-flame optimization algorithm optimized dual-mode controller for multiarea hybrid sources AGC system

OPTIMAL CONTROL APPLICATIONS & METHODS 2018年第2期39卷 720-734页

作者： Mohanty, Banaja Acharyulu, B. V. S. Hota, P. K. Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 India

A new algorithm called moth-flame optimization (MFO) algorithm is proposed to optimize a dual-mode controller (DMC) for a multiarea hybrid interconnected power system. Initially, a 2-area nonreheat system is considered. The optimum gains of DMC and proportional-integral controller are optimized using the MFO algorithm. The superiority of the proposed approach is established while comparing the results with genetic algorithm, bacterial forging optimization algorithm, differential evolution, and hybrid bacterial forging optimization algorithm particle swarm optimization for the same system. The proposed approach is further extended to 2 unequal areas of a 6-unit hybrid-sources interconnected power system. The optimum gain of DMC and sliding mode controller (SMC) is optimized with MFO algorithm. The performance of an MFO tuned DMC is compared with particle swarm optimization and genetic algorithm tuned DMC, MFO tuned SMC, and teaching-learning-based optimization optimized SMC for the same system. Furthermore, robustness analysis is performed by varying the system parameters from their nominal values. It is observed that the optimum gains obtained for nominal condition need not be reset for a wide variation in system parameters.

关键词： automatic generation control dual-mode controller generation rate constraint moth-flame optimization algorithm

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moth-flame optimization algorithm Based on Adaptive Weight and Simulated Annealing 8th

Moth-Flame Optimization Algorithm Based on Adaptive Weight a...

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8th International Conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Zhang, Qiang Liu, Li Li, Chengfei Jiang, Fan Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China

ISBN: (纸本)9783030026981;9783030026974

moth-flame optimization algorithm has the demerit of being easily trapped in local optimum. To solve this problem, an improved algorithm ASMFO is proposed in this paper. Adaptive weight can be automatically changed so that the algorithm can get a greater search scope in the early stage and the precision of the optimal solution can be increased in the later stage of the algorithm. Moreover, the simulated annealing method is employed to accept new solutions with a certain probability, which can further alleviate the problem that MFO is easy to fall into local optimum and will also enhance the global search ability of MFO algorithm. The experimental results show that the improved algorithm is superior to other optimization algorithms in the convergence precision and the stability.

关键词： moth-flame optimization algorithm Adaptive weight Simulated annealing

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Global and Local moth-flame optimization algorithm for UAV Formation Path Planning Under Multi-constraints

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第3期21卷 1032-1047页

作者： Wu, Xiao-Jing Xu, Lei Zhen, Ran Wu, Xue-Li Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China

To improve the global and local search ability of moth-flame optimization algorithm, three optimization strategies are proposed in this paper, namely chaos-based moth initialization, adaptive weighted position update strategy and population diversity improvement strategy. In moth initialization process, chaos-based Logistic map is adopted to improve population diversity. Then, a nonlinear weighting factor is introduced into the spiral function to adaptively balance the global and local search ability. Besides, new moth is generated by population diversity improvement strategy, which improves diversity and optimality of the population. Finally, simulation tests of unmanned aerial vehicle (UAV) formation under multi-constraints are carried out and comparison results show that the proposed global and local moth-flame optimization algorithm has the superiority in rapidity and optimality in UAV path planning problem compared with the latest path planning algorithms.

关键词： Global search local search moth-flame optimization algorithm path planning unmanned aerial vehicle

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A moth-flame optimization algorithm for web service composition in cloud computing: Simulation and verification

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SOFTWARE-PRACTICE & EXPERIENCE 2018年第10期48卷 1865-1892页

作者： Ghobaei-Arani, Mostafa Rahmanian, Ali. A. Souri, Alireza Rahmani, Amir Masoud Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Univ Amsterdam Informat Inst Amsterdam Netherlands Univ Human Dev Comp Sci Sulaymaniyah Iraq

In recent years, users are becoming increasingly accustomed to using the Internet to gain software resources in the form of web services provided by information technology organizations. Cloud computing is a service delivery paradigm that shares services and resources to access the web services to the end users over the Internet. In the cloud environment, based on the user's needs, various types of services with similar functionalities but different quality-of-service (QoS) criteria can be delivered, which often must be combined to meet the users' requests. The optimal selection and composition of these services are realized as an interesting issue. In this paper, we propose a moth-flame optimization (MFO) algorithm, which is a novel nature-inspired metaheuristic paradigm for the web service composition (WSC) problem called MFO-WSC, to improve the QoS criteria in the distributed cloud environment. Also, formal modeling is presented for the QoS-aware MFO-WSC algorithm with the model checking approach that receives the particular benefits to collaborate the correctness of the proposed algorithm. The correctness of the proposed behavior model is examined using some logical problems such as deadlock-free, fairness, and reachability conditions in the new symbolic model verifier model checker. The experimental results indicate the effectiveness of the proposed algorithm in comparison with similar related works.

关键词： cloud computing formal verification moth-flame optimization algorithm QoS web service composition

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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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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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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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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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Mathematical formulation and an improved moth-flame optimization algorithm for parallel two-sided disassembly line balancing based on fixed common stations

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第1期10卷 233-249页

作者： Zhang, Yu Zhang, Zeqiang Yin, Tao Liang, Wei Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China

Nowadays, rapid product iterations result in large quantities of end-of-life products. To meet the fast-growing demand for remanufacturing engineering, companies have quickened the standardization and industrialization of waste dissembling. Two-sided disassembly lines can effectively disassemble large-sized products on both sides of the lines, and parallel disassembly lines can disassemble multiple products simultaneously with fewer workstations and higher production efficiency. Combining the two types of disassembly can effectively increase the disassembly efficiency of large-sized products. However, the parallel two-sided disassembly line has not been fully investigated because of the essential complexity of the problem. Therefore, this research introduced the parallel two-sided disassembly line balancing problem with fixed common stations. First, a multi-objective mixed-integer programming model is established to solve the problem for the first time. The model is proved to be correct through small-scale numerical examples. Second, a multi-objective improved moth-flame optimization algorithm is implemented to solve the proposed large-scale problems. The proposed algorithm employs a two-phase decoding approach to design the scheme and a discrete moth for fire operation to search and replace new individuals, and then a restart strategy is introduced to reduce the probability of the population falling into a local optimum. Finally, the algorithm solved extensive disassembly line balancing problems with different layouts, including the straight-line, two-sided, and parallel two-sided, and case studies demonstrated the reliability and validity of the proposed method.

关键词： disassembly line balancing parallel two-sided disassembly line mixed-integer programming model moth-flame optimization algorithm multi-objective optimization

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Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved moth-flame optimization algorithm for Intelligent Route Planning of Multiple UAVs

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IEEE ACCESS 2022年 10卷 49385-49397页

作者： Ma, Mingxi Wu, Jun Shi, Yue Yue, Longfei Yang, Cheng Chen, Xuyi Air Force Engn Univ Air Control & Nav Coll Xian 710051 Peoples R China Air Force Engn Univ Equipment Management & UAV Engn Coll Xian 710051 Peoples R China 93787 Army Beijing 102600 Peoples R China

UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.

关键词： Planning optimization Radar Convergence Missiles Genetic algorithms Autonomous aerial vehicles Route planning moth-flame optimization algorithm cauchy mutation random opposition-based learning chaotic mapping

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