Multi-Strategy Fusion Improved Adaptive Hunger Games Search

Aiming at the drawbacks of Hunger Games Search (HGS) algorithm, such as slow convergence speed and the tendency to fall into local optimum, a Multi-strategy fusion Improved Adaptive Hunger Games Search (MIA-HGS) algorithm is proposed. Firstly, a good point set is employed to generate a more diverse initial population. Secondly, the control strategy selection parameter is fixed in the original HGS algorithm; an adaptive adjustment parameter is proposed to replace the fixed parameters, whose dynamically tuned update strategy strengthens the global searching ability. Finally, to further jump out of the local optimum, a mutation operation based on Logarithmic spiral opposition-based learning is performed on a population for a certain condition. Simulation experiments are carried out for 23 benchmark functions and the UAV aerial planning problem. The results show that MIA-HGS solves more accurately and converges more rapidly than the original HGS algorithm on 23 benchmark functions, with MIA-HGS leading on 69.5% of the tested functions and tying with HGS on 21.7% of the tested functions. It also showed better performance than the other algorithms on the UAV flight planning problem.