In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain.

The objective of this paper was to investigate the evolution of air voids (AV) and deformation and the failure mechanism of porous asphalt (PA) mixtures under high temperatures. An advanced repeated loading permanent deformation (ARLPD) test was used to investigate the high-temperature permanent deformation of PA mixtures. A staged ARLPD test (first stage test, secondary stage test, and third stage test) and a staged X-ray computed tomography (CT) scanning test were designed to evaluate the three-stage evolution of the AV and deformation in the ARLPD test. Total AV content ($A{V}_{\mathrm{total}}$ ) and interconnected AV content ($A{V}_{\mathrm{interconnected}}$ ) decrease in the first stage, and $A{V}_{\mathrm{total}}$ , $A{V}_{\mathrm{interconnected}}$ , and the number of AV ($A{V}_{\mathrm{num}}$ ) increase in the secondary stage. $A{V}_{\mathrm{total}}$ and $A{V}_{\mathrm{interconnected}}$ decrease and $A{V}_{\mathrm{num}}$ increases in the tertiary stage. Based on the changes of AV and intuitive image analysis, the failure mechanism of PA mixtures was discussed. The first stage is a densification stage, during which radial deformation develops faster than vertical deformation. Densification failure was observed in the tertiary stage, which shows that vertical deformation develops faster than radial deformation.

Addressing challenges like ocean currents, dynamic obstacles, collision avoidance, and computing limitations, a hybrid algorithm is proposed for multi-AUVs to search for random static or dynamic targets in a 3D marine environment and the environment is characterized by our Task Urgency Biological Neural Network (TUBNN) model, encapsulates factors such as currents, obstacles, and targets using neuronal activity representations. For optimizing task assignments in the face of unpredictable oceanic variables, a Double Layer Biological Self-organizing Map (DLBSOM) algorithm is proposed. Post assignment, the initial path, generated via DLBSOM, is refined by the end-to-end direct connection model to eliminate superfluous routes. Enhanced by the A $^\ast$ algorithm and Fermat’s spiral curve, our approach ensures paths align with AUV navigational constraints. Integrating real-time sonar data, our Distance-Speed Comprehensive Evaluation (DSCE) strategy, combined with the local dynamic neural network, facilitates the avoidance of unforeseen barriers, ensuring safe AUV navigation. The correction redistribution mechanism, leveraging live data and assessing AUV and target statuses, refines task assignment. We holistically evaluate environmental, cooperative, and AUV-specific constraints, with our algorithm’s efficacy validated through rigorous theoretical analysis, simulations, and field tests.

A trans-medium aircraft is a new concept aircraft that can both dive in the water and fly in the air. In this paper, a new type of water–air multi-medium span vehicle is designed based on the water entry and exit structure model of a multi-rotor UAV. Based on the designed structural model of the cross-media aircraft, the OpenFOAM open source numerical platform is used to analyze the single-medium aerodynamic characteristics and the multi-medium spanning flow analysis. The rotating flow characteristics of single-medium air rotor and underwater propeller are calculated by sliding mesh. In order to prevent the numerical divergence caused by the deformation of the grid movement, the overset grid method and the multiphase flow technology are used for the numerical simulation of the water entry and exit of the cross-medium aircraft. Through the above analysis, the flow field characteristics of the trans-medium vehicle in different media are verified, and the changes in the body load and attitude at different water entry angles are also obtained during the process of medium crossing.