| Citation: | HU Yaolong, FENG Qiang, HAI Xingshuo, et al. Improved pigeon-inspired optimization algorithm based on adaptive learning strategy[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2348-2356. doi: 10.13700/j.bh.1001-5965.2019.0603(in Chinese)
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Pigeon-Inspired Optimization (PIO) algorithm has been widely used in the field of UAV formation and control parameter optimization, but the standard PIO algorithm is easy to fall into local optimum. This paper proposes an Adaptive Learning Pigeon-Inspired Optimization (ALPIO) algorithm. The algorithm introduces a tolerance-based search direction adjustment strategy, a self-learning candidate generation strategy, and a competitive learning based prediction strategy. By enhancing the diversity of the population, the global optimal probability of the algorithm can be improved. The algorithm has been tested on eight benchmark functions. The simulation results show that the convergence accuracy and convergence speed of the algorithm in the multi-peak function optimization problem are significantly improved, and it can effectively avoid falling into the local optimal solution.
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