A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution

TL;DR

This paper analyzes how different constraint features affect the difficulty of constrained continuous optimization problems for e-Constrained Differential Evolution, providing insights into problem hardness related to constraint characteristics.

Contribution

It introduces a feature-based analysis of evolved instances to identify which constraint features influence problem difficulty for the algorithm.

Findings

Certain constraint features increase problem difficulty.

Evolved instances reveal key constraint characteristics affecting performance.

Insights can guide the design of more effective constrained optimization algorithms.

Abstract

Different types of evolutionary algorithms have been developed for constrained continuous optimization. We carry out a feature-based analysis of evolved constrained continuous optimization instances to understand the characteristics of constraints that make problems hard for evolutionary algorithm. In our study, we examine how various sets of constraints can influence the behaviour of e-Constrained Differential Evolution. Investigating the evolved instances, we obtain knowledge of what type of constraints and their features make a problem difficult for the examined algorithm.