Accurate and quick acquisition of hydrogeological parameters is the critical issue for groundwater numerical simulation and sustainability of the water sources. A novel intelligent inversion method of hydrogeological parameter, based on the global optimization algorithm called the disturbance-inspired equilibrium optimizer (DIEO), is developed. Firstly, the mathematical model and the framework of DIEO are reported. Several types of mathematical benchmark functions are used to test the performance of the DIEO. Furthermore, the intelligent inversion of hydrogeological parameters of pumping tests is transformed into the global optimization problem, which can be solved by meta-heuristic algorithms. The objective function for hydrogeological parameter inversion is constructed, and the novel inversion method based on DIEO is finally proposed. To further validate the competitiveness and efficiency of the proposed intelligent inversion method, three types of case studies are carried out. The results show that the proposed intelligent inversion method is reliable for obtaining the hydrogeological parameters accurately and quickly, providing a reference for the inversion of parameters in other fields.

The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.