Multi-Objective Ant Lion Optimizer (ALO) Algorithm

The Ant Lion Optimizer (ALO) algorithm simulates the natural hunting mechanism of antlions. The five primary stages include ants' random walks, trap construction, entrapment of prey, prey capture, and trap reconstruction. This algorithm is particularly suitable for solving multi-objective optimization problems. The original implementation typically involves population initialization, fitness evaluation, and iterative position updates using roulette wheel selection and adaptive boundary shrinking mechanisms.

Ant Lion Optimization Algorithm: A Nature-Inspired Approach for Single-Objective Optimization

The Ant Lion Optimization (ALO) algorithm mimics the natural hunting mechanism of antlions, consisting of five key steps: random walk of ants, trap construction, entrapment of prey, prey capture, and trap rebuilding. This algorithm is primarily designed for solving single-objective optimization problems. Key implementation aspects include stochastic movement modeling, fitness-based selection mechanisms, and adaptive boundary control. Credit and copyright belong to the original authors.