A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation

TL;DR

This paper compares various evolutionary algorithms on constrained continuous optimization problems, analyzing how different constraint features affect algorithm performance and identifying key factors influencing their effectiveness.

Contribution

It introduces a feature-based comparison framework for evolutionary algorithms on constrained problems, highlighting the impact of constraint features on algorithm performance.

Findings

Different evolutionary algorithms perform variably depending on constraint features.

Linear and quadratic constraints influence the difficulty of optimization.

Constraint features significantly affect the success of different algorithms.

Abstract

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaining close to optimal solutions. Using a multi-objective approach, we evolve constrained continuous problems having a set of linear and/or quadratic constraints where the different evolutionary approaches show a significant difference in performance. Afterwards, we discuss the features of the constraints that exhibit a difference in performance of the different evolutionary approaches under consideration.