A comparative Analysis of Two Multiobjective Metaheuristic Methods using Performance Metrics

Abstract

This paper provides an overview of the current state of research on multi-objective problems and compares two multi-objective metaheuristic methods: Multi-Objective Artificial Bee Colony (MOABC) and Non-Dominant Sorting Genetic Algorithm II (NSGA-II). The study evaluates the performance of these methods using three multi-objective test functions and three metrics: Generational Distance (GD), Spacing (SP), and Computational Time (CT). The results show that MOABC is the most suitable algorithm for multi-objective problems in terms of convergence and robustness, as indicated by the evaluation metrics.