A Similarity-Based Multiobjective Evolutionary Algorithm for Deployment Optimization of Near Space Communication System

The deployment of the airships plays a key role in maximizing the performance of the near space communication system. The main problem is how to strike a balance between the conflicting network speed and coverage for complex user distribution. In this paper, we propose a multiobjective deployment optimization model considering path loss, user demand, and inner structure. Under the framework of the multiobjective evolutionary algorithm (MOEA) based on decomposition (MOEA/D), we propose a similarity-based MOEA to optimize this problem. The proposed algorithm is motivated by the population's perception on the decision variable space. The proposed algorithm perceives the decision variable space by deploying airships to latent regions. The perceptions of different solutions are related by the similarity between their deployments and utilized differently by crossover and mutation. The proposed algorithm is tested on five designed problems compared with MOEA/D with the other popular reproduction operators. We also test the proposed scheme integrated with another two popular algorithms. The experimental results show that the similarity-based MOEA/D outperforms the other algorithms significantly in detecting hotspots, tracking multiple hotspots and safely deploying airships for most cases. The proposed scheme also works well with the other algorithms.