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...
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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 optimizationalgorithms in the convergence precision and the stability.
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 ...
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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.
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.
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.
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 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.
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 industrializatio...
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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.
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 optimizationalgorithm has stronger applicability and ...
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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 optimizationalgorithm 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.
A new algorithm called moth-flameoptimization (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 considere...
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A new algorithm called moth-flameoptimization (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 optimizationalgorithm, differential evolution, and hybrid bacterial forging optimizationalgorithm 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.
Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization me...
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Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization method combining the moth-flameoptimization (MFO) algorithm and fuzzy logic controller (FLC) is proposed to address the problem of poor adaptability of the parameter tuning method in the conventional power system stabilizer (CPSS). This method can optimize the parameters of PSS in different processes. Initially, the optimal parameters of PSS under the current perturbation are given by the MFO algorithm. During the online operation of the system, as perturbation changes, the parameters of the PSS will also be adaptively tuned by the FLC in real-time when the system operating conditions change. According to this method, a fuzzy adaptive proportional-integral-differential (FPID) controller is designed based on the moth-flame optimization algorithm (MFO-FPID), and it is used as PSS to improve dynamic stability performance during oscillation. Moreover, its parameters can be adaptively adjusted in different perturbation scenarios. The designed MFO-FPID controller is applied to the single machine infinite bus (SMIB) power system to compare the dynamic performance with other controllers, that is, proportional-integral-differential (PID) and CPSS. The result shows that the MFO-FPID controller can suppress the oscillation very well, and the control effect is the best.
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