ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

TL;DR

The paper introduces ANA, a novel swarm intelligence algorithm inspired by ant nest-building behavior, which effectively optimizes complex real-world engineering problems and outperforms several existing metaheuristics in benchmark tests.

Contribution

ANA is a new continuous swarm algorithm inspired by ant behavior, utilizing a unique weight-based rate of change for optimization, distinct from traditional ant colony algorithms.

Findings

ANA outperforms GA, PSO, DA, WOA, SSA, and FDO on benchmark functions.

ANA achieves competitive results in engineering problem optimization.

The algorithm demonstrates robustness and efficiency in real-world applications.

Abstract

In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm's capability in optimizing real-world problems.