Study of Decomposition and Ants Allocation in Multi-Objective Partial Classification Rule Induction

Abstract

Multi-objective evolutionary algorithms based on decomposition (MOEA/D) have established a dominant optimization paradigm successfully extended using Ant Colony Optimization (ACO). However, a rigid architectural assumption within this hybrid framework is the strict assignment of a single ant to evaluate each scalar subproblem, leaving the optimal distribution of computational resources largely unexplored. To address this, we introduce MOEA/D-AM, a hybrid algorithm that adapts this decomposition strategy for partial classification rule induction. By formulating rule induction to optimize multiple objectives simultaneously, we investigate how balancing the number of decomposed subproblems and ants impacts performance under a fixed computational budget. We evaluated five distinct resource configurations across nine benchmark datasets using two global storage strategies, a Pareto archive and a Memory archive. Our analysis reveals significant behavioral differences between these strategies under varying configurations. The findings highlight that the architectural distribution of subproblems and swarm resources fundamentally dictates the model's predictive capabilities. By tuning this trade-off, practitioners can tailor the rule-based classifier to the specific error costs of their domain.