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

In the field of optimization algorithms, the Ant Lion Optimization (ALO) represents a nature-inspired approach modeled after the predatory behavior of antlions in their natural habitat. The algorithm systematically implements five crucial hunting phases: random walk of ants (implemented through stochastic path generation), trap construction (modeled via fitness-based selection), entrapment of prey (achieved through adaptive boundary reduction), prey capture (executed by fitness evaluation and replacement), and trap reconstruction (facilitating iterative optimization). This algorithm is particularly suitable for solving single-objective optimization problems, where developers can implement it using population-based iteration with elitism preservation. The core mathematical operations involve random walk simulations using cumulative sum functions, radius adaptation through decreasing parameters, and position updates using roulette wheel selection. While copyright and intellectual property rights belong to the original creators, their contribution deserves recognition for advancing optimization methodology and enabling practical applications in engineering design, parameter tuning, and computational intelligence tasks. The algorithm's implementation typically requires 30-50 lines of code in platforms like MATLAB or Python, featuring main functions for initialization, fitness evaluation, and iterative optimization loops.