MOANA: Multi-objective Ant Nesting Algorithm
Yasameen Sami, Tarik Ahmed Rashid, Aso Mohammed Aladdin · 2025
An Ant Nesting Algorithm (ANA) Optimizer was recently developed, and the aim of this research is to demonstrate its multi-objective version. The acronym for this technique is MOANA, which stands for Multi-objective ANA. Modern multi-objective algorithms aim to achieve solid outcomes by pursuing two primary objectives: convergence and diversity maintenance. In order to fulfil these objectives, the fundamental ideas formed by Indicator-based Evolutionary Algorithms are used in this thesis: the Roulette Wheel Selection guide, the hypercube grid guide, archiving, and the genetic operator. The performance of the proposed method is evaluated using several test functions known as “traditional ZDT test functions”, in addition to a real-world situation known as the welded beam design challenge. The outcomes of MOANA are evaluated and contrasted with those of the most current iterations of Multi-Objective Fitness Dependent Optimization, the Multi-objective Learner Performance-Based Behavior Algorithm, Multi-objective Particle Swarm Optimization (MOPSO), the Non-Dominated Sorting Genetic Algorithm Third Improvement (NSGA-III), and the Multi-objective Dragonfly Algorithm (MODA). Finally, MOANA is programmed using MATLAB. According to the result, the proposed strategy is successful in maintaining a balance between convergence and diversity.