MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems

TL;DR

MOANA is a new multi-objective ant nesting algorithm that improves solution diversity and convergence speed, outperforming existing methods on benchmark datasets and real-world engineering problems.

Contribution

Introduces MOANA, an extension of ANA with adaptive mechanisms and mutation strategies, enhancing multi-objective optimization performance.

Findings

Superior convergence speed compared to state-of-the-art algorithms.

Better Pareto front coverage on benchmark datasets.

Effective application to real-world engineering problems.

Abstract

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks. Comparative analysis against state-of-the-art algorithms like MOPSO, MOFDO, MODA, and NSGA-III demonstrates MOANA's superior performance in terms of convergence speed and Pareto front coverage. Furthermore, MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions, making it a practical tool for decision-makers. MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios, positioning it as a robust solution for complex optimization tasks.