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The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems. [ABSTRACT FROM AUTHOR]

Path planning is one of the most critical issues in many related fields including UAVs. Many researchers have addressed this problem according to different conditions and limitations, but modelling the 3-D space and routing with an evolutional algorithm in such spaces is an open issue. So, in this paper, we first, introduce a method to grids the environment using geometrical shapes. This can reduce the random states of cell decomposition and increases the computational speed. We then propose an effective routing algorithm based on the butterfly optimization algorithm (BOA). It can simultaneously optimize multiple path planning objectives. It uses an objective function to compute the shortest path, based on obstacle avoidance and the UAV's operational power minimization. A novel concept, the intelligent throwing agent, used in this algorithm prevents getting stuck in local optima and increases the network coverage in path planning. The throwing agent prevents the collision of the UAV with the obstacles using geometrical techniques and contour lines. The simulation results show that BOA has the least and second-least cost in best-case and worst-case scenarios in comparison with ant colony and particle swarm. Its run time and the optimal value of the fitting function are also better than the two other algorithms. [ABSTRACT FROM AUTHOR]