<abstract><p>The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of Aquila birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially in high complex ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search. The developed algorithm named as mAO was tested using 29 CEC 2017 functions and five different engineering constrained problems. The results prove the superiority and efficiency of mAO in solving many optimization issues.</p></abstract>
This paper proposed a photodetector with a dual-injection structure. Input power is splitted into two beams by a 3dB beam splitter at the incident end and then coupled into the photodetector, which can make the light field distribution in the germanium absorption layer more uniform. With the same active area structure, the responsivity of the dual-injection structure can be increased by 45.73 % at 108.33 mW, the saturated optical power could be induced by 44.76 %, and the bandwidth can be improved by 6.74 GHz at 20 mW comparing with the single-injection structure. The dual-injection structure improves the performance of the device without increasing the complexity.
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