An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios

TL;DR

This study investigates how different control parameters of MOEA/D affect its performance under two distinct evaluation scenarios, revealing that optimal settings vary significantly with the scenario.

Contribution

It provides the first comprehensive analysis of MOEA/D control parameters under different performance evaluation scenarios, highlighting their scenario-dependent effects.

Findings

Parameter settings significantly influence MOEA/D performance.

Optimal parameters vary greatly between scenarios.

Understanding scenario-specific effects improves MOEA/D tuning.

Abstract

An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evolutionary algorithms (MOEAs) in recent studies. A recent benchmarking study also shows that decomposition-based MOEA (MOEA/D) is competitive with state-of-the-art MOEAs when the UEA is incorporated into MOEA/D. However, a parameter study of MOEA/D using the UEA has not yet been performed. Thus, it is unclear how control parameter settings influence the performance of MOEA/D with the UEA. In this paper, we present an analysis of control parameters of MOEA/D under two performance evaluation scenarios. One is a final population scenario where the performance assessment of MOEAs is performed based on all nondominated solutions in the final population, and the other is a reduced UEA scenario where it is based on a pre-specified number of selected nondominated solutions from the UEA. Control parameters of MOEA/D investigated in this paper include the population size, scalarizing functions, and the penalty parameter of the penalty-based boundary intersection (PBI) function. Experimental results indicate that suitable settings of the three control parameters significantly depend on the choice of an optimization scenario. We also analyze the reason why the best parameter setting is totally different for each scenario.