Antlion Optimization Algorithm (ALO)

The Antlion Optimizer (ALO) is a swarm intelligence optimization algorithm inspired by the predatory behavior of antlions in nature. It mimics the process where antlions construct conical traps, wait for prey to fall in, and capture them. This algorithm demonstrates superior global search capabilities and convergence speed compared to traditional optimization methods like Particle Swarm Optimization (PSO) or Genetic Algorithms (GA).

The core concept of ALO treats candidate solutions as antlions and ants - antlions build traps within the search space while ants perform random walks. The algorithm dynamically adjusts the positions of both antlions and ants to gradually approach the optimal solution. Its key strength lies in balancing exploration and exploitation capabilities, preventing premature convergence, making it particularly suitable for complex nonlinear optimization problems.

In practical implementations, ALO parameters such as population size (number of ants) and maximum iterations need to be configured according to problem scale. The algorithm's performance has been validated in various fields including engineering optimization and neural network training. Compared to other algorithms, ALO demonstrates stronger robustness when solving high-dimensional problems.

From a coding perspective, the algorithm typically involves initializing random positions for both antlions and ants, implementing random walk mechanisms with boundary checks, calculating fitness values, and updating positions through selection operations where fitter antlions influence ant movements. Key functions would include trap building simulation, random walk implementation with decreasing boundaries, and elite selection mechanisms.