Multi-Strategy Fusion Improved Adaptive Hunger Games Search
Daming Zhang, Yanqing Zhao, Junjie Ding, Zijian Wang, Jiaqing Xu
Abstract
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.