ALO 算法核心思想是模拟蚁狮捕猎蚂蚁的狩猎机制以实现全局寻优。蚁狮在捕猎前会在在沙质土中利用其巨大的下颚挖出一个漏斗状的陷阱，并藏在陷阱底部等待猎物到来。一旦随机游走的蚂蚁落入陷阱时，蚁狮迅速将其捕食，随后重新修缮陷阱等待下一次捕猎。(The core idea of the ALO algorithm is to simulate the hunting mechanism of the ant lion hunting ants to achieve global optimization. Before hunting, the ant lion will use its huge jaw to dig a funnel-shaped trap in the sandy soil and hide at the bottom of the trap to wait for the prey to arrive. Once the randomly-wandering ants fall into the trap, the ant lion quickly preys on it, and then repairs the trap to wait for the next hunt. The ALO algorithm realizes the interaction between the ants and the ant lion through numerical simulation to optimize the problem: introduces the random walk of the ants to realize the global search, and guarantees the diversity of the population and the optimization performance of the algorithm through the roulette strategy and the elite strategy. Antlion is equivalent to the solution of the optimization problem. It can update and save the approximate optimal solution by hunting ants with high fitness.)