Improved Black Hole Algorithm for Intelligent Traffic Navigation

Traffic navigation is an important part of intelligent transportation systems (ITSs). In this paper, we propose a novel algorithm for path searching based on an improved black hole (BH) method to enhance the real-time navigation efficiency. The original BH algorithm is optimized firstly. Parallel evolution, and information exchange strategy inspired by the quasi-affine transformation evolution (QUATRE) algorithm, allow agents with effective information to search the solution space quickly and effectively that can prompt the convergence speed and expand the diversity of solutions. At the same time, strengthen the exploitation around the global best solution also can enable agents to find the target quickly. The performance of our algorithm can be confirmed by CEC 2017 benchmark functions. For practical purposes, the optimal path should not only consider the shortest distance, but also the minimum fuel consumption. Besides, the effectiveness and timeliness are of great significance for real-time path navigation. In the simulation system, our proposed algorithm can reduce the error rate of navigation to find a realistic path. The results indicate that the proposed algorithm for navigation is effective and stable