A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evolution (HGWO). Because basic grey wolf optimization (GWO) is easy to fall into stagnation when it carries out the operation of attacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE's strong searching ability. The proposed algorithm can accelerate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.

Fengyun-3E (FY-3E), the world's first early-morning-orbit meteorological satellite for civil use, was launched successfully at the Jiuquan Satellite Launch Center on 5 July 2021. The FY-3E satellite will fill the vacancy of the global early-morning-orbit satellite observation, working together with the FY-3C and FY-3D satellites to achieve the data coverage of early morning, morning, and afternoon orbits. The combination of these three satellites will provide global data coverage for numerical weather prediction (NWP) at 6-hour intervals, effectively improving the accuracy and time efficiency of global NWP, which is of great significance to perfect the global earth observing system. In this article, the background and meteorological requirements for the early-morning-orbit satellite are reviewed, and the specifications of the FY-3E satellite, as well as the characteristics of the onboard instrumentation for earth observations, are also introduced. In addition, the ground segment and the retrieved geophysical products are also presented. It is believed that the NWP communities will significantly benefit from an optimal temporal distribution of observations provided by the early morning, mid-morning, and afternoon satellite missions. Further benefits are expected in numerous applications such as the monitoring of severe weather/climate events, the development of improved sampling designs of the diurnal cycle for accurate climate data records, more efficient monitoring of air quality by thermal infrared remote sensing, and the quasicontinuous monitoring of the sun for space weather and climate.

We propose a new meta-heuristic algorithm named Levy flights multi-verse optimizer (LFMVO), which incorporates Levy flights into multi-verse optimizer (MVO) algorithm to solve numerical and engineering optimization problems. The Original MVO easily falls into stagnation when wormholes stochastically re-span a number of universes (solutions) around the best universe achieved over the course of iterations. Since Levy flights are superior in exploring unknown, large-scale search space, they are integrated into the previous best universe to force MVO out of stagnation. We test this method on three sets of 23 well-known benchmark test functions and an NP complete problem of test scheduling for Network-on-Chip (NoC). Experimental results prove that the proposed LFMVO is more competitive than its peers in both the quality of the resulting solutions and convergence speed.

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