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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.

Recently, numerous new meta-heuristic algorithms have been proposed for solving optimization problems. According to the Non-Free Lunch theorem, we learn that no single algorithm can solve all optimization problems. In order to solve industrial engineering design problems more efficiently, we, inspired by the algorithm framework of the Arithmetic Optimization Algorithm (AOA) and the Harris Hawks Optimization (HHO), propose a novel hybrid algorithm based on these two algorithms, named EAOAHHO in this paper. The pinhole imaging opposition-based learning is introduced into the proposed algorithm to increase the original population diversity and the capability to escape from local optima. Furthermore, the introduction of composite mutation strategy enhances the proposed EAOAHHO exploitation and exploration to obtain better convergence accuracy. The performance of EAOAHHO is verified on 23 benchmark functions, the IEEE CEC2017 test suite. Finally, we verify the superiority of the proposed EAOAHHO over the other advanced meta-heuristic algorithms for solving four industrial engineering design problems.

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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.

Due to the large volume of pipeline transportation, low cost, safety and the reliability, and automatic control, it is widely used in many fields of industrial development and human daily life. Most of the traditional hydraulic pipelines are steel pipes, and their structure is simple. High resistance and high consumption during transportation are not conducive to the sustainable development of society. However, the human vascular system is intricate and has excellent mechanical properties. Built on the review, research on the fluid-solid coupling characteristics of a single bionic pipeline and piping system was carried out. In order to simulate the mechanical characteristics of a fluid conveying pipeline, a fluid-structure coupling model of equation 14 of a single pipeline and the transfer matrix of the pipeline system were established. The mechanical characteristics of the pipeline are studied, and the formula is calculated. The simulation analysis shows that the axial force and flow resistance decrease first and then stabilize with the increase of frequency. Finally, the experimental verification and the results show that the method is both reasonable and effective, because the simulation curve and the experimental curve are consistent in trend.

We present a bionic neural wave network that uses multiple autonomous underwater vehicles to search and acquire intelligent targets in an unknown underwater environment. The neuron pheromone content is arranged according to neural wave diffusion and layer-by-layer energy attenuation, when underwater mesh space based on neural wave diffusion theory was established that the neuron nodes in the neural network structure correspond to obstacles, autonomous underwater vehicles, and targets in the environment. In order to solve the problems of over-allocation and under-allocation of the multi-autonomous underwater vehicles system during the cooperative capture of targets, a redistribution mechanism based on the improved self-organizing map algorithm is implemented and directed to rationalize task distribution. Two different taboo search methods are employed to update the autonomous underwater vehicle path in real time, and the polynomial coefficient solution method is used to fit partial path data. So that the autonomous underwater vehicle trajectory can be obtained and an interceptor position coordinate can be predicted. An auxiliary autonomous underwater vehicle is aimed to replace the intercepted autonomous underwater vehicle and the matching capture points are tracked to ensure the completion of the task so that the full range of hunting targets is identified. In order to simulate an unknown complex underwater environment, obstacles are randomly arranged around the target, the location information of the obstacle, and the target is unknown and unpredictable. Four simulation experiments were performed to verify the accuracy and efficiency of the algorithm under unknown environment. The results show that this algorithm can improve the path update average efficiency by 66% compared with other algorithms. Obviously, this algorithm is reasonable and effective.

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