ALO Optimization Algorithm: An Ideal Solution for Grey Model (GM) Enhancement

The Ant Lion Optimizer (ALO) is an intelligent optimization algorithm inspired by the predatory behavior of antlions in nature, particularly suitable for parameter tuning scenarios like Grey Model (GM) optimization. By simulating how antlions construct traps in sand to capture prey, ALO demonstrates superior exploration capabilities compared to traditional Particle Swarm Optimization (PSO). In code implementations, this behavior translates to a multi-phase search strategy where "antlion pits" represent potential solutions and "ants" correspond to candidate parameters undergoing optimization.

Key advantages include: Parameter Boundary Control: ALO enables explicit definition of upper and lower bounds for optimization parameters, effectively preventing boundary violation issues common in PSO. This feature is particularly valuable for GM models requiring strict parameter constraints. Implementation-wise, this involves embedding boundary checks directly in the position update equations, ensuring parameters remain within predefined physical meaningful ranges. Adaptive Search Mechanism: The algorithm dynamically adjusts search scope through the "trap radius" concept, facilitating broad exploration initially and progressive refinement toward convergence. This balances global and local search capabilities through intelligent step size adaptation coded via radius decay functions. Premature Convergence Avoidance: Unlike PSO's tendency to stagnate in local optima, ALO's random walk mechanism for prey movement increases the probability of escaping local minima. This is algorithmically achieved by incorporating stochastic perturbations and Lévy-flight-like movements in the search process.

In applications with GM(1,1) and other grey models, ALO efficiently optimizes key parameters such as the development coefficient and background values, significantly improving prediction accuracy. Its inherent boundary constraint characteristics naturally align with GM's requirements for parameter physical meaning (e.g., non-negativity), which can be directly enforced through constraint handling mechanisms in the objective function evaluation.