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Section 3 proposes the adaptive mobile crowd optimization.
In view of the above achievements and problems, based on the multi-objective optimization model, we proposed adaptive mobile crowd optimization based on distance field.
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Figure 5 shows the ACO-DF (mobile adaptive crowd optimization scheme based on distance field) algorithm and the positioning error of the target optimization algorithm based on mobile search denoted as OO-MS.
The crowd control model can design the mobile constraints and crowd optimization objectives based on the real-time mobile trajectory information.
Fig. 3 Mobile crowd biological engineering optimization opportunistic control architecture.
We proposed the mobile crowd biological engineering optimization opportunities control mechanism.
The real-time decision of the upper control center is supported by mobile target trajectory control from mobile crowd multiple target optimization.
On the basis of the above model and probability calculation, Fig. 3 shows the mobile crowd biological engineering optimization opportunities to control architecture.
In "Section 3," we design the mobile crowd biological engineering opportunistic optimization control mechanism.
According to the method of Fig. 3, based on mobile positioning and crowd control, crowd optimization model is obtained for predicting and optimizing the moving goal, as well as the crowd acquisition.
For complexity and efficiency of the multi-objective optimization, we proposed the mobile distance field-driven adaptive crowd optimization algorithm.
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