Eagle eye algorithm combined with depth of field control and lens imaging for cross-field applications
There is relatively less research on cross-field general algorithms,and the effectiveness needs to be further improved.Aiming at this problem,an eagle eye optimization algorithm for cross-field problems that combined Depth of Field Control(DFC)and Lens Imaging Learning(LIL)was proposed.The DFC and LIL strategies were introduced into the eagle eye of the Golden Eagle Optimization(GEO)algorithm.The distance between the golden eagle and prey was converted into the distance between the shooting focus and camera focus plane.Combined with other shooting parameters,the front and back depth of field,the focal point of the mirror and the actual position of the object were calculated,and the position of new golden eagle individual was updated.In addition,to test the generality of the algorithm,the performances of different algorithms on 56 open-source test functions and 5 cross-field datasets were compared.The experimental results indicated that the proposed algorithm had some competi-tiveness.
eagle eye optimizationdepth of field controlswarm intelligence algorithmscross-field optimizationengineering applications
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