Multi-Objective Ant Lion Optimizer (ALO) Algorithm

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Resource Overview

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.

Detailed Documentation

In this article, we present the Ant Lion Optimizer (ALO) algorithm, which draws inspiration from the natural hunting behavior of antlions. The algorithm mimics five key hunting phases: random walks of ants, trap construction, prey entrapment, prey capture, and trap reconstruction. ALO is particularly effective for solving multi-objective optimization problems. In practical implementations, the algorithm maintains two populations (ants and antlions) and employs elite selection strategies to guide the search process. Key computational steps involve normalizing solution positions, applying random walk operations with boundary checks, and adaptively updating trap sizes based on fitness values. Note that copyright of the original work belongs to the respective authors, and we pay tribute to the original creators.

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