This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism for the first time. The Van der Waals force is applied to the pheromone updating mechanism. Simulation results show that the improved algorithm has advantages in quality and speed of solution compared with other mainstream algorithms. This paper provides fast and accurate route methods for solving the Traveling Salesman Problem first and a delivery scheme is also presented for UAVs to realize “contactless delivery” to users in the Changchun Mingzhu District during the COVID-19 epidemic, which confirms the practicability and robustness of the algorithm.

This paper proposes a hybrid swarming algorithm based on Ant Colony Optimization and Physarum Polycephalum Algorithm. And the Van Der Waals force is first applied to the pheromone update mechanism of the hybrid algorithm. The improved method can prevent premature convergence into the local optimal solution. Simulation results show the proposed approach has excellent in solving accuracy and convergence time. We also compare the improved algorithm with other advanced algorithms and the results show that our algorithm is more accurate than the literature algorithms. In addition, we use the capitals of 35 Asian countries as an example to verify the robustness and versatility of the hybrid algorithm.

Surface plasmon coupled emission (SPCE) is the directional emission of surface plasmon polaritons (SPPs) through the reverse channels of focused surface plasmon excitation to the far field, which has shown significant possibilities in bioanalysis, medical diagnosis, and so on. We carried out a theoretical study of SPCE to analyze its mechanisms and proposed a new structure to improve the emission intensity of SPCE. We proposed a method for refractive index sensing based on SPCE, consisting of a reverse Kretschmann (RK) or a Tamm structure for the first time, to the best of our knowledge. The corresponding sensing sensitivity reaches 87.61 deg/RIU and 67.44 deg/RIU, respectively. Compared with that in the RK, the far-field radiation intensity of SPCE in the Tamm structure is enhanced by two orders of magnitude. Furthermore, compared with surface plasmon resonance (SPR) sensing, SPCE sensing can improve the signal-to-noise ratio (SNR) and excitation efficiency. Our structures enable refractive index sensing with a high SNR, high spatial resolution, and without the requirement of angular alignment using complex mechanics, which are suitable for practical applications such as quantitative biomolecular detection and medical diagnosis.

This paper presents an improved Discrete Salp Swarm Algorithm based on the Ant Colony System (DSSACS). Firstly, we use the Ant Colony System (ACS) to optimize the initialization of the salp colony and discretize the algorithm, then use the crossover operator and mutation operator to simulate the foraging behavior of the followers in the salp colony. We tested DSSACS with several algorithms on the TSP dataset. For TSP files of different sizes, the error of DSSACS is generally between 0.78% and 2.95%, while other algorithms are generally higher than 2.03%, or even 6.43%. The experiments show that our algorithm has a faster convergence speed, better positive feedback mechanism, and higher accuracy. We also apply the new algorithm for the Wireless rechargeable sensor network (WRSN) problem. For the selection of the optimal path, the path selected by DSSACS is always about 20% shorter than the path selected by ACS. Results show that DSSACS has obvious advantages over other algorithms in MCV’s multi-path planning and saves more time and economic cost than other swarm intelligence algorithms in the wireless rechargeable sensor network.