As an emerging power inspection method, unmanned aerial vehicle (UAV) inspection has the advantages of high safety, high efficiency, and low cost. In the process of power inspection, UAVs need to inspect multiple task points in a complex environment and plan an efficient and feasible path. In this research, the multiple UAVs inspection in the two cases of initial task points and newly added task points is considered. Aiming at these two cases, a hybrid algorithm is proposed in this paper. Firstly, the personal example learning strategy is applied to the golden eagle optimizer (GEO) to get a personal example learning GEO called PELGEO to improve the search ability of the GEO and reduce the possibility of GEO falling into a local optimum. Secondly, the grey wolf optimizer (GWO) is simplified and the differential mutation strategy is introduced to create the simplified GWO with differential mutation called DMSGWO. Finally, to give full play to the advantages of the PELGEO and the DMSGWO, an adaptive hybridization strategy is used to hybridize PELGEO and DMSGWO. The new hybrid algorithm based on GEO and GWO named HGEOGWO is proposed. The HGEOGWO and other algorithms are tested under the CEC2013 test suite. The experimental results show that the HGEOGWO has better optimization performance and stability than some popular algorithms. For the 3D path planning problem of multiple UAVs in power inspection, the proposed algorithm also has obvious advantages compared with some popular algorithms. The code of HGEOGWO can be publicly available at https://www.mathworks.com/matlabcentral/fileexchange/97807-a-new-hybrid-algorithm-based-on-geo-and-gwo .

Node localisation is a common and significant practical application question in wireless sensor network (WSN). The goal of this problem is to use anchor nodes in the network to estimate the geographical location of the unknown node. A novel algorithm, named adaptive multi-group slime mould algorithm (AMSMA), is proposed in this study. The improved slime mould algorithm uses the multi-group strategy and adaptive communication mechanism to alleviate the lack of population diversity, development and exploration imbalance of the slime mould algorithm. The proposed AMSMA was tested under CEC2013 test suite. Compared with SMA and corresponding optimisation algorithms, the AMSMA is more effective and efficient. In addition, a novel localisation algorithm based on AMSMA is proposed. The AMSMA-Distance Vector-Hop (AMSMA-DV-Hop) is applied to the localisation of WSN. Compared with some other existing localisation algorithms, the proposed AMSMA-DV-Hop is an effective algorithm for the localisation of WSN.

Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved; secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation.