A Multi-Agent Evolutionary algorIthm for Connector-Based Assembly Sequence Planning

AbstractSome Evolutionary algorithms for connector-based ASP have been researched. But those algorithms have lots of blind searching because individuals have little intelligence in making use of geometry and assembly process information of product assembly body. To improve individuals’ intelligence, A multi-agent evolutionary algorithm for connector-based ASP (MAEA-ASP) is presented which is integrated with the multi-agent systems. learning, competition and crossover -mutation are designed as the behaviors of agent which locate lattice-like structure environment. Experimental results show that MAEA-ASP can find an approximate solution faster compared with other evolutionary algorithms

Triple-dip La Niña in 2020–23: understanding the role of the annual cycle in tropical Pacific SST

The triple-dip La Niña in 2020–23 is characterized by persisting southeasterly wind anomalies over the tropical central and eastern Pacific. Our results show that the wind anomalies are associated with the anomalously negative phase of the first two leading modes of the annual cycle (antisymmetric and symmetric modes about the equator) of sea surface temperature (SST) in the tropical Pacific. The two modes account for 82.2% and 13.5% of the total variance, linking to the seasonal swing of SST between the northern and southern hemispheres and the temporal evolution of El Niño-Southern Oscillation, respectively. During 2020–23, the persistently and anomalously negative phase of the symmetric mode enhances easterly wind over the tropical central Pacific, while the antisymmetric mode strengthens the southeasterly wind over the tropical eastern Pacific. The anomalously negative phase of the antisymmetric mode is associated with the contrast of SST anomalies between the northern and southern hemispheres, which provided a favorable background for the triple-dip La Niña in 2020–23

Microstructure and Wear of W-Particle-Reinforced Al Alloys Prepared by Laser Melt Injection

W-particle-reinforced Al alloys were prepared on a 7075 aluminum alloy surface via laser melt injection to improve their wear resistance, and the microstructure, microhardness, and wear resistance of the W/Al layers were studied. Scanning electron microscopy (SEM) results confirmed that a W/Al laser melting layer of about 1.5 mm thickness contained W particles, and Al4W was formed on the surface of the Al alloys. Due to the reinforcement of the W particles and good bonding of the W and Al matrix, the melting layer showed excellent wear resistance compared to that of Al alloys