Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

Abstract

As millimeter waves will be widely used in the Internet of Things (IoT) and Telematics to provide high bandwidth communication and mass connectivity, the coverage optimization of base stations can effectively improve the quality of communication services. How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers. This paper proposes the Muti-Fusion Sparrow Search Algorithm (MFSSA) optimize the situation to address the problem of discrete coverage maximization and rapid convergence. Firstly, the initial swarm diversity is enriched using a sine chaotic map, and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability. Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a "precocious" state. Such a swarm is very suitable for solving NP-hard problems. Secondly, an elite opposition-based learning strategy is added to expand the search range of the algorithm, and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum. This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm. Finally, the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem, and the convergence speed is better than other algorithms. MFSSA is improved by more than 10% compared to the original algorithm.