In recent years, the increasingly severe water pollution problem encouraged researchers to optimize water quality monitoring sensor networks (WQMSNs) by creating new underwater sensor coverage algorithms. Since the sensor is limited by the monitoring range and the number of targets, optimizing the 3D target coverage of heterogeneous multisensors is essential to maximize the 3D target coverage rate of the monitored waters. To enhance the target coverage rate, the target allocation needs to be searched in all possible combinations. To optimize the 3D coverage of underwater targets, this research proposes a chaotic parallel artificial fish swarm algorithm (CPAFSA). CPAFSA uses chaotic selection to initialize parameters and integrates the global search capabilities of parallel operators. It also applies the elite selection which effectively avoiding local optimization and solving the problem of 3D target coverage. Ultimately, CPAFSA is compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results of the simulation experiment demonstrated the excellent performance of CPAFSA in achieving underwater 3D target coverage.

In Part II, we explored the decay processes of the moderate and relatively‐weak El Niño. The results show that for the moderate El Niño, the anomalous signals appear in the development and peak phase, then they decay quickly after the peak phase due to the insufficient intensity of the El Niño. This is similar to the abortion of the western Pacific oscillation process. For this reason, different from the strong El Niño, the moderate El Niño turns into a normal state. For the relatively‐weak El Niño, its phase transition is controlled by another mechanism. With the enhancement and westward movement of the subtropical high over the southeastern Pacific, the easterly and sea surface temperature anomalies also extend westward from the tropical eastern Pacific. The above process is an advective mode that causes the relatively‐weak El Niño turning into a La Niña.