A novel high-level target navigation pigeon-inspired optimization for global optimization problems

The canonical Pigeon-inspired optimization (PIO) possesses excellent local exploitation capability and can provide a very fast convergence speed, but it is also easily trapped in local optima especially when facing complex problems. In order to take full advantage of the superior local exploitation capability, and at the same time enhance the global exploration capability of PIO, we present a modified PIO called high-level target navigation PIO (HTNPIO). The HTNPIO includes three strategies, selective mutation strategy (SMS), levy-based map-compass strategy (LMS), and enhanced landmark strategy (ELS). In the strategies, two kinds of mutation strategies and a simple levy-flight operator are performed. What's more, an LMS-ELS probability is proposed to balance the exploration and exploitation. In order to test the performance of the proposed optimizer, HTNPIO is made comparisons with other 15 PIO and advanced heuristic algorithms on the IEEE CEC2017 benchmark problems and 5 real world optimization problems. Experimental results demonstrate that HTNPIO defeats all the competitors on the CEC2017 benchmark problems including the extraordinarily competitive LSHADE, and also exhibits extremely competitive performance in dealing with the real-world problems. Therefore, HTNPIO might be effective to provide promising solutions in various function and industrial optimization problems.