RESUMO
In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.
RESUMO
This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population's diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.
RESUMO
The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.
