【摘要】 沙猫群算法是一种新颖的群智能优化算法。为了进一步提高该算法的收敛精度，避免陷入局部最优问题，引入自适应t分布和Lévy飞行机制改进沙猫群算法，称之为TLSCSO算法。首先设计一个非线性收敛因子，用来平衡算法的探索和开发，再引入自适应t分布，提高收敛精度，最后引入Lévy飞行机制，使算法跳出局部最优。在CEC 2022的12个测试函数上与其他算法进行对比，计算结果表明，TLSCSO在大部分测试函数上能够找到更好的解，且收敛速度快。更多还原

【Abstract】 The sand cat swarm algorithm is a novel swarm intelligent optimization algorithm. In order to further improve the convergence accuracy of this algorithm and avoid falling into the local optimization problem,adaptive t-distribution and Lévy flight mechanism are introduced to improve the sand cat swarm algorithm,which is called TLSCSO algorithm. Firstly,a nonlinear convergence parameter is designed to balance the exploration and development of the algorithm. The adaptive t-distribution is introduced to improve the convergence accuracy,and finally,the Lévy flight mechanism is introduced to make the algorithm jump out of the local optimum. Compared with other algorithms on 12 test functions of CEC 2022,the computational results show that TLSCSO is able to find better solutions on most of the test functions and converges fast.更多还原