A feature-based analysis on the impact of linear constraints for ε-constrained differential evolution

Abstract

Feature-based analysis has provided new insights into what characteristics make a problem hard or easy for a given algorithms. Studies, so far, considered unconstrained continuous optimisation problem and classical combinatorial optimisation problems such as the Travelling Salesperson problem. In this paper, we present a first feature-based analysis for constrained continuous optimisation. To start the feature-based analysis of constrained continuous optimization, we examine how linear constraints can influence the optimisation behaviour of the wellknown e-constrained differential evolution algorithm. Evolving the coefficients of a linear constraint, we show that even the type of one linear constraint can make a difference of 10-30% in terms of function evaluations for well-known continuous benchmark functions.