Network-Based Heterogeneous Particle Swarm Optimization and Its Application in UAV Communication Coverage

Particle swarm optimization (PSO) aims at finding the optimum point in a high-dimension solution space by simulating the swarming and flocking behaviors in nature. Recent empirical studies of reconstructing the hidden interaction networks in flocking birds and schooling fish found that individuals play different roles in group decision making. An outstanding question is whether the performance of PSO can be improved by incorporating these empirical findings. Here, we systematically explore the impact of the heterogeneity of interaction network and individual's learning strategies to find that the corresponding network-based algorithm, network-based heterogeneous particle swarm optimization (NHPSO), significantly outperforms other PSO based and non-PSO-based comparative algorithms on our experiments with 18 test functions. Our further analysis of the information exchange among the particles reveals that learning from low-degree particles in the middle period of evolution is crucial for the swarm to achieve the global optimum. These results offer a new framework to improve the performance of swarm optimization and demonstrate the applicability of network science in designing optimization algorithms. Finally, the universality of NHPSO is demonstrated on an emerging application, the unmanned aerial vehicle communication coverage.