Deployment of optoelectronic devices based on improved NSGA-II

In order to improve the security of the defended targets in our region and efficiently utilize various types of optoelectronic devices, the research is conducted on optoelectronic devices deployment within the region. With the objectives of maximizing the protection effectiveness and minimizing devices operational cost, a multi-objectives optimization model is established by considering constraints such as devices-target visibility conditions and device types, quantities, etc. Non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ) improved by Q-learning is designed to solve the model. To address the difficulty of fixed parameter settings adapting to dynamic changes, Q-learning is adopted to adaptively adjust the crossover probability and variation probability. In order to search for Pareto front solutions that are close to the global optimal more efficiently. The correctness of the model and the effectiveness of the algorithm are verified through simulation examples.