基于自适应学习策略的改进鸽群优化算法

鸽群优化（PIO）算法已广泛用于无人机编队和控制参数优化等领域，但标准PIO算法容易陷入局部最优。提出了一种基于自适应学习策略的改进鸽群优化（ALPIO）算法。该算法引入了基于容差的搜索方向调整策略、基于自学习的候选者生成策略以及基于竞争学习的预测策略，通过增强种群的多样性，可提高算法全局最优概率，其已在8个基准函数上进行测试。仿真试验结果表明:所提算法在多峰函数优化问题中的收敛精度和收敛速度有了显著提升，并且能够更有效避免陷入局部最优解。 

Improved pigeon-inspired optimization algorithm based on adaptive learning strategy

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.