Title：Meta-heuristic algorithms for the applications of wireless network planning
[Abstract] Meta-heuristics algorithms are widely used to solve various optimization problems. The concepts are usually inspired by the rules of nature. For example, the Simulate Anneal algorithm (SA) learns from the process of crystal cooling. Genetic algorithms (GA) follow the selection, crossover, and mutation processes of biological. The concept of ant colony optimization (ACO) comes from the foraging behavior of ant colonies. The implementation of these algorithms is flexible and efficient. In particular, when compared with the current popular deep learning algorithms, less amount of input data and fewer computing resources are required. This talk will cover my previous works on meta-heuristic algorithms for wireless network planning, including small cell deployment, traffic offloading, and resource allocation in B5G mobile networks as well as the charger planning for a wireless rechargeable sensor network.