Helmholtz coils are widely used in biomedicine, aerospace, and other fields due to their simple structure and remarkable magnetic properties. However, the uniform areas they generate are not suitable for conducting sc...
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Helmholtz coils are widely used in biomedicine, aerospace, and other fields due to their simple structure and remarkable magnetic properties. However, the uniform areas they generate are not suitable for conducting scientific experiments with large equipment. In order to improve the uniformity and scale of the uniform magnetic field, a novel scheme is proposed in this paper. Two auxiliary coils are added on both sides of the Helmholtz coils to optimize the magnetic field. The non-dominatedsortinggeneticalgorithm (NSGA-ii) was used to optimize the structural parameters of the improved model, while orthogonal experiments were conducted to obtain initial values for better adaptability of influencing factors. A comparison was made between the magnetic field deviation rates of the coil optimized by traditional derivative algorithms within a range of 0.2 m similar to 0.8 m from the central axis of the magnetic field. Finally, finite element simulation was employed to verify the calculated results. The results show that the magnetic field deviation rate of the coil is less than 0.5% when the intelligent algorithm is used for optimization in the range of 0.6 m from the central axis. In addition, the structure can be extended to a three-dimensional structure, which can achieve a constant magnetic field in any direction by controlling the size and direction of the current, making it a potential application prospect in magnetic navigation technology.
This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wi...
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This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominatedsorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm ii (NSGAii) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAii method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.
The Vehicle Routing Problem's focal point is no longer a single objective to get a shortest routing or satisfy customer's time demand,but a multiple objective which include nearly every aspect in the distribut...
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The Vehicle Routing Problem's focal point is no longer a single objective to get a shortest routing or satisfy customer's time demand,but a multiple objective which include nearly every aspect in the distribution network such as the cost of delivery,shipping time,and distance of *** of these aspects are very significant in the delivery *** VRP models cannot take real-time reaction to the traffic *** this paper,a two-layer model including multiple objective and realtime road information is *** geneticalgorithm,NSGA ii and ESGA,is used to optimize this *** optimization algorithm could find the optimal result very soon.
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