Self-learning Antelopes Migration Algorithm (SAMA) is a self-heuristic algorithm that simulates the local exploitation of ordinary antelopes and the global exploration of scout antelopes. Aiming at the imbalance between exploitation and exploration of SAMA, it is easy to fall into local optimum, and the convergence speed and precision will be affected. A pseudo-parallel chaotic self-learning antelope migration algorithm based on mobility model is proposed. Nine chaotic self-learning antelope migration algorithms are formed by incorporating nine kinds of chaotic local searching operators, and the total population is divided into several sub-populations through meme grouping, and the internal circulation optimization is carried out by using nine chaotic SAMAs. After the internal cycle is completed, the five migration models will perform their migration operations and mutation operations to form the pseudo-parallel chaotic SAMA to increase the diversity of the population, improve the optimization accuracy and the ability of the algorithm to balance exploitation and exploration. Three simulation experiments are carried out to verify the effectiveness of the proposed algorithm. Firstly, the chaotic SAMA and the pseudo-parallel chaotic SAMA based on the mobility models are used to optimize 17 benchmark test functions. Secondly, 25 test functions in CEC-BC-2017 are optimized respectively. Finally, the four engineering design problems are optimized, including three-bar truss design, welded beam design, pressure vessel design and spring design problems. Experimental results show that the improved algorithm can better solve the function optimization and engineering optimization problems. The pseudo-parallel chaotic SAMA based on the mobility models has the advantage of balancing exploitation and exploration in the optimization process, and improves the convergence accuracy.

自学习羚羊迁移算法（SAMA）是一种自我启发式算法，模拟普通羚羊的局部开发和侦察羚羊的全局探索。针对SAMA的开发与探索不平衡，容易陷入局部最优，影响收敛速度和精度。提出了一种基于移动模型的伪并行混沌自学习羚羊迁移算法。结合九种混沌局部搜索算子形成九种混沌自学习羚羊迁移算法，通过模因分组将总种群划分为若干子种群，利用九种混沌SAMA进行内部循环优化。内部循环完成后，五种迁移模型将进行迁移操作和变异操作，形成伪并行混沌SAMA，以增加种群的多样性，提高优化精度和算法平衡开发和探索的能力。进行了三个仿真实验来验证所提出算法的有效性。首先，利用基于移动模型的混沌SAMA和伪并行混沌SAMA对17个基准测试函数进行优化。其次，分别优化了CEC-BC-2017中的25个测试功能。最后对四个工程设计问题进行了优化，包括三杆桁架设计、焊接梁设计、压力容器设计和弹簧设计问题。实验结果表明，改进后的算法能较好地解决函数优化和工程优化问题。基于移动模型的伪并行混沌SAMA在优化过程中具有平衡开发和探索的优点，提高了收敛精度。