Caching plays a key role in reducing latency and increasing the overall performance of foggy computing systems. Edge and foggy computing has emerged as a major challenge in the response to latency- and bandwidth-sensitive processing in intelligent navigation systems Information-based networks (ICN) and foggy computing (ICN-Fog) has emerged as an optimal solution in low-latency, high-throughput applications, and a leap forward in reducing latency and achieving better data communication and efficient information classification for foggy computing. Air navigation systems have been deployed. The use of artificial intelligence (AI) and firefly optimization methods as an effective optimization algorithm in the promotion of cushioning technique has been introduced and investigated. In this study, the main goal of improving performance indicators such as cache hit ratio, internal link load, and average system response time is to optimize an optimal algorithm. This technique has been applied to the ICN-Fog caching model and has been investigated and modeled to determine the location of the cache and compliance with the network topology. The multi-objective firefly algorithm is equal to 3572 and the standard deviation is equal to 725. The success rate of the algorithm in convergence to the optimal point is equal to 99%. The results show that the multi-objective firefly algorithm (MOFA) will provide better performance and higher reliability factor in intelligent air navigation systems compared to other algorithms in terms of efficiency and effectiveness in identifying the optimal caching technique.